Revisiting the value of polysomnographic data in insomnia: more than meets the eye

Journal Pre-proof Revisiting the value of polysomnographic data in insomnia: more than meets the eye Thomas Andrillon, Geoffroy Solelhac, Paul Bouchequet, Francesco Romano, Max-Pol Le Brun, Marco Brigham, Mounir Chennaoui, Damien Léger PII:

S1389-9457(19)31644-2

DOI:

https://doi.org/10.1016/j.sleep.2019.12.002

Reference:

SLEEP 4251

To appear in:

Sleep Medicine

Received Date: 19 September 2019 Revised Date:

22 November 2019

Accepted Date: 4 December 2019

Please cite this article as: Andrillon T, Solelhac G, Bouchequet P, Romano F, Le Brun M-P, Brigham M, Chennaoui M, Léger D, Revisiting the value of polysomnographic data in insomnia: more than meets the eye, Sleep Medicine, https://doi.org/10.1016/j.sleep.2019.12.002. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier B.V. All rights reserved.

Author Contribution Statement Conceptualization: TA, GS, DL; Data curation: GS, PB, FR, DL; Formal analysis: TA, GS, PB, MPLB, MB; Funding acquisition: DL; Investigation: TA, GS, PB, M-PLB, MB, DL; Methodology: TA, GS, PB, FR, M-PLB, MB, DL; Project administration: DL; Resources: DL, MC; Software: PB, GS, M-PLB, MB; Supervision: DL; Validation: DL; Visualization: TA, GS, PB; Roles/Writing - original draft: TA, GS, DL; Writing - review & editing: TA, GS, PB, FR, M-PLB, MB, MC, DL.

Highlights: - PSG allows to significantly differentiate good sleepers from subjects with insomnia. -

EEG spectrum and sleep microstructure reveal common physiological signatures of insomnia.



-

Insomnia seems characterized by increased cortical excitability during sleep.

-

AI classifiers allow to classify good sleepers from insomnia patients.

-

AI and PSG should be used more extensively in the diagnosis of insomnia

1

Revisiting the value of polysomnographic data in insomnia: more than

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meets the eye

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Thomas Andrillon a-b, Geoffroy Solelhac a-c*, Paul Bouchequet a-c*, Francesco Romano a-c,

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Max-Pol Le Brund, Marco Brigham d, Mounir Chennaoui a-e & Damien Léger a-c.

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a. Université de Paris, Equipe d'accueil VIgilance FAtigue SOMmeil (VIFASOM) EA 7330,

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Paris, France;

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b. School of Psychological Sciences and Turner Institute for Brain and Mental Health,

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Monash University, Melbourne, Victoria, Australia

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c. Assistance Publique-Hôpitaux de Paris (APHP) Hôtel Dieu, Centre du Sommeil et de la

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Vigilance, Paris, France

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d. Ecole Polytechnique, Centre de mathématiques appliquées (CMAP), Palaiseau, France

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e. Institut de recherche biomédicale des armées (IRBA), Brétigny-sur-Orge, France

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*: equal contribution,

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Corresponding authors: [email protected]

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Abstract (max. 250)

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BACKGROUND: Polysomnography (PSG) is not recommended as a diagnostic tool in

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insomnia. However, this consensual approach might be tempered in the light of two ongoing

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transformations in sleep research: big data and artificial intelligence (AI).

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METHOD: We analyzed the PSG of 347 patients with chronic insomnia, including 59 with

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Sleep State Misperception (SSM) and 288 without (INS). 89 good sleepers (GS) were used as

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controls. PSGs were compared regarding: (1) macroscopic indexes derived from the

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hypnogram, (2) mesoscopic indexes extracted from the electroencephalographic (EEG)

30

spectrum, (3) sleep microstructure (slow waves, spindles). We used supervised algorithms to

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differentiate patients from GS.

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RESULTS: Macroscopic features illustrate the insomnia conundrum, with SSM patients

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displaying similar sleep metrics as GS, whereas INS patients show a deteriorated sleep.

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However, both SSM and INS patients showed marked differences in EEG spectral

36

components (meso) compared to GS, with reduced power in the delta band and increased

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power in the theta/alpha, sigma and beta bands. INS and SSM patients showed decreased

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spectral slope in NREM. INS and SSM patients also differed from GS in sleep microstructure

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with fewer and slower slow waves and more and faster sleep spindles. Importantly, SSM and

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INS patients were almost indistinguishable at the meso and micro levels. Accordingly,

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unsupervised classifiers can reliably categorize insomnia patients and GS (Cohen’s =0.87)

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but fail to tease apart SSM and INS patients when restricting classifiers to micro and meso

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features ( =0.004).

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CONCLUSION: AI analyses of PSG recordings can help moving insomnia diagnosis beyond

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subjective complaints and shed light on the physiological substrate of insomnia.

47 48

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49 50 51

Highlights: - PSG allows to significantly differentiate good sleepers from subjects with insomnia. -

EEG spectrum and sleep microstructure reveal common physiological signatures of insomnia.

52 53

-

Insomnia seems characterized by increased cortical excitability during sleep.

54

-

AI classifiers allow to classify good sleepers from insomnia patients.

55

-

AI and PSG should be used more extensively in the diagnosis of insomnia

56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

Keywords: Artificial intelligence-machine learning-insomnia-polysomnography-REM-NREM sleepsupervised classification Abbreviations NREM: Non-Rapid Eye Movement REM: Rapid Eye Movement PSG: Polysomnography AI: Artificial Intelligence ML: Machine Learning EEG: Electroencephalography CI: Chronic Insomnia SSM: Sleep State Misperception SOL: Sleep Onset Latency TST: Total Sleep Time: WASO: Wake After Sleep Onset

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Acknowledgements This work was supported by the Banque Publique d’Investissement (BPI) supporting the

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MORPHEO project associating three partners: RHYTHM (Paris, France); Université Paris

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Descartes (Paris, France) and Ecole Polytechnique (Palaiseau, France). TA is supported by

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the International Brain Research Organization (IBRO), the Human Frontiers Science Program

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(HFSP, LT000362/2018) and the Australian National Health and Medical Research

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(NHMRC, ECF-APP11614980).

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Introduction

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Sleep is a need, like eating or breathing [1,2]. Yet, we are not equal in the face of sleep

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and some individuals have a hard time fulfilling this basic physiological imperative [3,4]. In

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particular, individuals suffering from Chronic Insomnia (CI, here referred as “insomnia”) are

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characterized by difficulties in initiating or maintaining sleep and/or a chronic sensation of

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non-restorative sleep. The associated sleep deficit has substantial daytime consequences,

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notably in terms of well-being, productivity and health [5]. Insomnia is the most common

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sleep disorder and affects between 10 and 20 % of the general population of industrialized

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countries [6,7], which makes it a major public health concern. Accordingly, insomnia has

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been associated with a broad range of comorbidities: it has been linked to an increased risk of

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cardiovascular diseases or diabetes [8–13], psychiatric disorders (anxiety, depression, suicide)

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[11,14], cognitive impairments and neurodegenerative disorders [11,15,16].

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There is a growing literature on the physiological underpinnings of insomnia (see [17–

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19] for reviews). Yet, contrary to other sleep disorders, insomnia is almost never diagnosed or

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assessed via objective physiological recordings. This is particularly striking when considering

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that sleep is usually described as a phenomenon “of the brain, by the brain, for the brain”

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[20]. The main reason advanced is that PSG recordings are costly and often fail to reveal

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sleep anomalies despite clear subjective complaints of CI [19]. Besides, it is unclear if

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clinicians would treat patients differently with the additional information from PSG

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recordings. Indeed, the most consensual therapy of insomnia, the so-called cognitive

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behavioral therapy (CBT), is based mostly on subjective assessments of sleep quality and

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quantity by patients themselves [21]. Finally, it has been argued that a single night of PSG

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recordings is not enough to assess insomnia. Phenomena like the “first night effect” (FNE, i.e.

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deterioration of sleep when sleeping in a new environment, like a sleep clinic [22]) are

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regularly observed in good sleepers but can be often reversed in individuals with insomnia

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[23], questioning the reliability of PSG recordings as a diagnostic tool for insomnia.

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Consequently, the 3rd edition of the International classification of sleep disorders

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(ICSD-3) [24] recommends a diagnosis of insomnia purely based on subjective complaints. In

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this framework, CI severity is assessed by self-report questionnaires like the Pittsburgh Sleep

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Quality Index (PSQI) or the Insomnia Severity Index (ISI) [25,26]. PSG recordings are

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specifically limited to rule out comorbidities commonly associated with insomnia such as

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sleep apnea (OSA) or periodic leg movements (PLM).

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The limited use of PSG recordings in the diagnosis of insomnia could stem from the

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notoriously noisy relationship between subjective and objective estimates of sleep duration

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and sleep quality [19], although it could be argued that these discrepancies are precisely what

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makes PSG valuable since it provides complementary information to subjective reports. The

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largest objective epidemiological survey on insomnia, made in 1042 inhabitants of Sao Paulo

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Brazil, reported that only 35% of subjects with an ICSD-3 diagnosis of insomnia had a PSG

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confirming insomnia [27]. In addition, 23% of those who declare themselves good sleepers

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matched the PSG insomnia criteria [27]. The case of the so-called Sleep State Misperception

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(also referred to as “paradoxical insomnia”) is particularly telling as, in such cases, there is a

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direct contradiction between the subjective complaints of insomnia and the visual inspection

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of PSG recordings by sleep experts [28,29]. Importantly, even if insomnia patients with SSM

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do not show signs of a significantly degraded sleep, they still feel they have poor sleep and do

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present the daytime consequences of insomnia, which argues in favor of retaining the

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subjective complaints and sometimes discarding the information extracted from PSG

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recordings.

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Nonetheless, previous research has evidenced robust markers of insomnia within PSG

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recordings. For example, the spectral decomposition of the EEG signal allows tracking neural

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dynamics across vigilance states and evidenced differences between good sleepers and

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insomnia patients [30–33]. The discovery that insomnia is associated with heightened levels

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of high-frequency activity during sleep [30], a rhythm typically associated with wakefulness,

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supports the “hyperarousal model” of insomnia [17], that is the notion that individuals

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suffering from insomnia are hypersensitive to perturbations, notably during their sleep. Others

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have found increased levels of alpha oscillations during sleep, another wakeful pattern of

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brain activity that normally disappears during sleep [34]. Sleep also appears more fragile in

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insomnia: with more frequent micro-arousals [35] and the preservation of neural signatures of

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light sleep during deeper sleep stages [36]. This sleep fragility could be due to an increased

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sensitivity to external [39–41] or internal perturbations [42], according to the hyperarousal

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model. The fact that attentional networks seem more activated during sleep in insomnia

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patients [37,38] could explain this increased sensitivity to external and internal perturbations.

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PSG recordings also play a central role in the evaluation of insomnia treatments. Drug

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agencies, like the American Food and Drug Agency (FDA) require positive evidence from

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PSG studies to release new hypnotics [39]. The European Medicines Agency (EMA)

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guidelines [19,40] for assessing insomnia treatments consider PSG recordings “helpful” to

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diagnose insomnia and while drug efficacy is based on “clinically relevant improvement of

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subjective parameters”, the agency recommends to support these changes with objective data.

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In addition, PSG data is mandatory for new treatments’ proof of concept.

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However, even if PSG data contain clinically relevant information regarding insomnia,

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it does not necessarily imply its use is relevant in clinical settings. PSG are still a costly, time-

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consuming medical examination, both regarding its acquisition and analysis. Yet, rapid

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evolutions in the field of Sleep Medicine might offer ways to overcome these practical

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limitations. First, technological improvements make it now possible to easily and

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inexpensively record high-quality data at home, for multiple nights, with minimal impact on

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patients’ life and sleep [41,42]. Second, recent progress in artificial intelligence and machine

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learning have rendered the analysis of large, multi-modal datasets more efficient and

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clinically relevant [43,44]. In sleep medicine, such techniques have already transformed the

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evaluation of PSG recordings and the detection of specific sleep disorders [44–48]. We aim at

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showing here how similar tools can be applied to insomnia. First, we show it is possible to

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streamline the analysis of PSG recordings with minimal human intervention. Second, by

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devising automated, unbiased and exhaustive analyses of physiological data, we propose a

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reassessment of the benefits and costs of PSG recordings in the context of insomnia.

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To revisit the value of physiological data for the diagnosis and understanding of

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insomnia, we undertook a comprehensive analysis of a large dataset of PSG recordings,

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acquired in a classical clinical setting. Indeed, in the past few years, our sleep clinic (Center

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for Sleep and Vigilance of the Hôtel-Dieu Hospital in Paris) changed its clinical management

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of insomnia patients and propose a PSG to all referred patients with a complaint of insomnia

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lasting for at least 5 years. We present here an analysis of the comparison of these PSG

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recordings with good sleepers, deprived of insomnia symptoms. In particular, we focused on a

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multi-level analysis of PSG recordings. We examined classical hypnogram-based metrics of

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sleep quality and quantity, coupled with the analysis of EEG spectral decomposition and sleep

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microstructure (sleep spindles and slow waves). We further investigated how advanced

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analyses of PSG recordings can help the diagnosis of insomnia in difficult cases such as

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patients with SSM. Most importantly, we aimed at understanding how discrepancies between

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objective and subjective assessments of sleep can be resolved through a more in-depth

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analysis of PSG data.

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METHODS

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Participants

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In this study, we analyzed PSG recordings from 347 patients suffering from insomnia. These

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patients were selected retrospectively, following their visit to the Center for Sleep and

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Vigilance of the Hotel-Dieu Hospital in Paris (France). All the patients included in this study

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complained of CI for at least five years according to ICSD-3 criteria [19]. As part of their

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clinical assessment, these patients were proposed to undergo an ambulatory or laboratory

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PSG, whether or not comorbidities were suspected. All diagnoses were delivered by the

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medical doctors affiliated with the Center, independently of this retrospective study.

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Patients were diagnosed with CI according to the ICSD-3 criteria [24], i.e. whenever they

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reported (i) difficulties initiating sleep or sleep onset latency (SOL) ≥ 30 minutes, or (ii)

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difficulty maintaining sleep and/or early-morning awakenings (iii) at least three times a week,

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(iv) since at least three months and (v) with consequence on daytime activities.

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Patients were further screened for comorbidities through a visual inspection of the PSG

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recordings and according to the guidelines of the American Academy of Sleep Medicine

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(AASM) [45]. We excluded patients with a diagnosis of OSA (i.e. patients with a Respiratory

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Disturbance Index (RDI) >10) or a diagnosis of PLM disorder (criterion: >10/hour).

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We focused here on two subtypes of primary insomnia: patients with or without SSM, also

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respectively referred as “objective” and “subjective” insomnia in the literature.

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Insomnia without SSM:

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The so-called “objective” nature of insomnia was confirmed by examining PSG recordings to

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rule out a significant case of SSM, that is a discrepancy between patients’ reports of sleep

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quantity and quality and the quantification of sleep quantity and quality via PSG. Patients

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with at least one of the following PSG criteria were diagnosed with insomnia without SSM:

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SOL > 30 minutes, Wake After Sleep Onset (WASO)> 30 minutes, Total Sleep Time (TST) <

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360 minutes [19,27]. These patients are referred to as the INS group throughout (N=288

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patients; age: 45.7 ± 0.84 (mean ± Standard-Error of the Mean or SEM); sex ratio: 0.46).

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Insomnia with SSM:

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Patients with SSM were diagnosed based on (i) a subjective complaint of Chronic Insomnia

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for at least five years according to the ICSD-3 criteria and (ii) the absence of objective criteria

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of insomnia in PSG recordings (SOL ≤ 30 minutes, WASO ≤ 30 minutes, and TST ≥ 360

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minutes [22]). These patients are referred to as the SSM group throughout (N=59; age: 39.4 ±

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1.55; sex ratio: 0.31).

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Treatments:

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Patients were not asked to suspend their treatment for the night of PSG. Out of 347 patients,

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we had access to treatment information in 335 patients (12 patients with missing data). 132

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patients were taking benzodiazepines or benzodiazepine-like drugs (40% of INS patients;

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29% of SSM patients); 73, antidepressants (INS: 19%; SSM: 29%); 35, melatonin (INS: 10%;

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SSM: 9%); 17, antihistamines (INS: 5%; SSM: 3%) and 154 had no treatment.

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Controls:

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Patients were compared to individuals who did not report any sleep disorder and who were

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considered good sleepers (GS) after reviewing their PSG recordings. These individuals were

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originally recruited as part of research protocols conducted in the same sleep clinic. All these

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control subjects underwent one night of baseline PSG at the clinic or at home (similarly as

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patients). This baseline night, identical for all individuals and not affected by the details of

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each research protocol, was analyzed and compared to patients. Based on PSG recordings, we

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further discarded individuals with OSA or PLM. 100 individuals were reviewed and 89 good

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sleepers were retained as controls (age: 34.5 ± 1.33; sex ratio: 3.68).

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PSG recordings

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PSG were performed according to the AASM guidelines [46] and included: (i) at least 3 and

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usually 6 electroencephalographic (EEG) derivations at frontal (F3/F4), central (C3/C4) and

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occipital (O1/O2) sites and referenced to the contralateral mastoid, (ii) 2 electrooculographic

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(EOG) derivations and, (iii) 3 electromyographic (EMG) derivations placed on the chin (N=1)

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and legs (N=2). Respiratory parameters (respiratory flow, thoracic and abdominal bands,

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oxygen saturation), as well as body movements (position sensor and the 2 leg EMG

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derivations placed on the left and right tibial muscles), were also systematically recorded in

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order to exclude individuals with OSA and PLM syndromes.

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Different recording devices were used in this study, all validated and routinely used in our

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sleep clinic: the NOX-A1 (Nox Medical), ACTIWAVE (CamNTech LTd), MORPHEUS

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(Micromed S.p.A.) and SOMNOLOGICA (Medcare). INS patients were recorded with NOX-

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A1 (N=284) and MORPHEUS (N=4) devices. SSM patients with NOX-A1 (N=59) devices

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and good sleepers with NOX-A1 (N=6), ACTIWAVE (N=73) and SOMNOLOGICA (N=10)

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devices.

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Sleep scoring and sleep markers identification

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Immediately after the completion of the night recording, PSG data were visually examined

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and scored according to the AASM guidelines [47] to establish individual hypnograms. We

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also identified arousals, leg movements and respiratory events. Arousals were defined as an

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acceleration of the EEG for epochs between 3 and 15 seconds. This visual scoring was

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performed by a medical doctor specialized in sleep medicine. In practice, 30-s-long epochs

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including EEG (referenced to the mastoids), EMG and EOG data were scored as wakefulness

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(W), NREM stage 1 (N1), NREM stage 2 (N2), NREM stage 3 (N3) and REM sleep (REM).

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In addition to this initial sleep scoring and as part of this retrospective study, all files were

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blindly scored again by the same sleep technician (F.R.), with more than 5 years of

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experience in sleep medicine, to ensure the homogeneity of the sleep scoring across the entire

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dataset. From this second scoring, we extracted classical parameters from individual

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hypnograms reflecting the macro-structure of sleep: TTS, WASO, SOL, duration of each

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sleep stage, number and density of arousals, etc (see Table 1). Sleep cycles were also visually

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identified based on the hypnogram. No specific detection of physiological or non-

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physiological artefacts was performed beyond the ones relevant to establish a diagnosis.

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Based on the hypnograms, we also computed state-transition matrices for each night (Fig. 1),

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by looking at how sleepers transit from one sleep stage to another. To do so, we considered

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the individual hypnograms obtained on 30-s-long epochs. For each epoch, we computed the

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transition probability by considering the sleep stage of the following epoch. We then

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computed the proportion (probability) of each state transition. Figure 1 shows the resulting

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transition matrices.

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Identification of sleep disorders

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The same expert (F.R.) visually identified markers of sleep disturbances. Occurrences of

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apnea (respiratory flow below 10% for more than 10 seconds) and hypopnea (>30% decrease

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in respiratory flow associated with arousal or oxygen desaturation of more than 3% and for

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more than 10 seconds) were marked. For each apnea and hypopnea event, thoracic and

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abdominal bands were used to categorize these events as ‘central’, ‘obstructive’ or ‘mixed’.

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Leg periodic movements were identified and marked whenever 4 leg movements were

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observed over a period of 90s. All these markers of sleep disturbances were then quantified

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and the diagnosis of OSA or PLS was established based on existing guidelines [24]. All

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individuals with OSA and PLS were excluded from our analyses.

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REM sleep and Rapid Eye-Movements.

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The AASM guidelines do not propose a subdivision of REM sleep but previous studies

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sometimes distinguish between phasic (REM sleep epochs with at least one Rapid-Eye

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Movement on the EOG derivations) and tonic (REM sleep without Rapid-Eye Movement)

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REM sleep [48,49]. Rapid Eye-Movements in REM sleep have been associated with sleep

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depth, sensory isolation [48,50] and oneiric activity [51,52]. To examine whether insomnia

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was associated with a change in REM physiology and its associated oculographic activity, we

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visually identified Rapid Eye-Movements using the 2 EOG derivations. A REM was defined

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as an initial deflection in the EOG signal of less than 0.5 seconds, with phase opposition

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between the 2 EEG channels [51]. For each recording, we computed the total number of

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REMs and density of REMs (number of REMs/minutes spent in REM). We also computed

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the number and duration of individual REM epochs.

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Automatic detection of sleep spindles, K-complexes and slow waves

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NREM sleep stages 2 and 3 are characterized by the presence of specific patterns of brain

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activity [53,54]. These NREM sleep hallmarks are slow waves and K-complexes (large-

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amplitude [1-4]Hz waves occurring in isolation or train) and sleep spindles (waxing-and-

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waving [11-16]Hz oscillations lasting for 0.5 to 2.5s). To detect slow waves, K-complexes

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(both here referred as slow waves) and sleep spindles, and to extract relevant parameters from

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individual events, we relied on previously published algorithms [55–58].

294

In short, the detection of slow waves was performed by first filtering the EEG mastoid-

295

reference signal between 0.2 and 3Hz (zero-phase Finite Impulse Response (FIR) filter).

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Individual waves were identified as the interval between two descending zero-crossing. Only

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waves with a down-state duration (negative half-wave period) between 0.25s and 1s, and a

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peak-to-peak amplitude above 75 V were identified as slow waves. For each of these slow

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waves, we extracted the following parameters: wave frequency (1/wave duration), peak-to-

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peak amplitude and positive slope (slope from the down-state to the up-state of the slow

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wave).

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Sleep spindles were likewise detected by first filtering the mastoid-referenced EEG signal

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between 11 and 16Hz (zero-phase FIR filter). From the filtered signal, we extracted the

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Hilbert envelope, which gives an estimate of instantaneous power within the [11-16]Hz range.

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A threshold corresponding to the median envelope voltage + two standard-deviations (median

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+ 2 SD, computed on 30-s-long epochs) was computed and used to identify epochs of high

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spindle power. The beginning and end of each high spindle-power epoch were further

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determined using a more liberal threshold (median +1 SD) and these epochs were considered

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spindle candidates. Spindle candidates within 500ms were aggregated. These candidates were

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considered sleep spindles if: (i) their duration was comprised between 0.5 and 2.5s, (ii) they

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were not concomitant with large increases in high-frequency power ([60-90]Hz) typically

312

associated with artifacts, (iii) they did not cross a median + 10 SD threshold (also typically

313

associated with artifacts). For each of these detected spindles, we then extracted the following

314

parameters: the maximal amplitude (of the [11-16]Hz envelope), duration and frequency. The

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spindle frequency was estimated using a Fast-Fourier Transform (FFT) on a 1-s-long window

316

centered on the peak in spindle power (resolution: 1Hz).

317

For both the slow wave and sleep spindle detection, only events detected in NREM2 and

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NREM3 were analyzed. We applied the detection algorithms to central and frontal derivations

319

(C3, F3 or C4, F4) independently. Slow waves and spindles detection algorithms were

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implemented in Python and used the MNE toolbox [59,60]

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Spectral analysis

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The EEG signal is classically described as resulting from the superposition of neural

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oscillations [54]. To quantify these different rhythms, we performed a spectral decomposition

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of the EEG signal. The EEG spectrogram was obtained by applying an FFT to 30-s-long

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epochs of mastoid-referenced EEG data using the “eegkit” package in R. We then computed

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the relative power within the [0.5-20]Hz range by dividing the power in each frequency bin

327

with the sum of power values between 0.5 and 20Hz (step: 0.5Hz). The relative power was

328

expressed as a percentage.

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We applied a supervised classification approach for each frequency bin (Fig. 2a) in order to

330

evidence which frequency bins can differentiate between the 3 groups of sleepers (see

331

following section for the details of the training and testing of the classifiers). This procedure 11

332

was applied to the power spectrum averaged across NREM and REM epochs separately and

333

for each individual. We computed the ROC (Receiver Operating Characteristic) curve area

334

(chance level: 0.5; perfect performance: 1) to quantify the classifiers’ performance. We

335

repeated the supervised classification 100 times to estimate the variability of the performance

336

estimate. Cluster-permutations (see below) were used to compare the distribution of the

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classifiers’ performance (across the different frequency bins) to chance (0.5) or between

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NREM and REM sleep.

339

To examine how the EEG power was modulated across groups and throughout the night, we

340

binned NREM and REM epochs (N=51 bins) from the beginning (0) to the end of the night

341

(100%). Bonferroni-corrected ANCOVAs and cluster permutation were used to identify the

342

time-windows and frequency bands significantly modulated across groups (Fig. 2b).

343

The EEG signal is not only composed of periodic oscillations but also contains aperiodic

344

activity constituting the background EEG activity upon which neural oscillations develop

345

[61]. Recent work has stressed the relevance of such aperiodic activity and developed means

346

to quantify the strength of the aperiodic component of the EEG spectrum [62–64]. To

347

examine whether insomnia was associated with changes in aperiodic activity, we computed

348

the slope of the spectrum’s 1/f trend, an index of aperiodic activity. The slope of the EEG

349

spectrum was computed via the FOOOF (Fitting Oscillations and One-Over-F) package for

350

Python [61]. In short, the average EEG spectrum for a given recording and sleep stage was

351

obtained by averaging the FFT power spectrum computed over all the 30-s-long epochs for

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this sleep stage and recording. A first aperiodic fit was then computed for the average power

353

(P=f-s, where P is the power, f the frequency and s the slope, a variable parameter). This initial

354

fit was removed, evidencing deviations from the aperiodic background (i.e. peaks

355

corresponding to the different periodic oscillations). Each peak was modeled as a Gaussian.

356

The Gaussian fits were then used to remove the periodic peaks from the original spectrum and

357

a second aperiodic fit was performed to obtain an approximation of the spectrum without the

358

periodic activity (peaks). The slope of this second fit was used to estimate the slope of the

359

aperiodic spectrum.

360

Supervised classification

361

To determine whether features extracted from PSG recordings are predictive of insomnia and

362

insomnia sub-types, we tried to classify individuals in clinically-relevant diagnostic categories

12

363

using supervised classifiers. This supervised classification was performed in R using the caret

364

package (Classification And REgression Training).

365

Since our main goal was here to check whether quantitative analyses of PSG recordings could

366

help the diagnosis of insomnia, we trained and tested classifiers focusing on different

367

contrasts. The first classifier was trained to predict two categories of individuals (2-class

368

classifier): controls (good sleepers, GS) vs. patients with CI (with or without SSM:

369

SSM+INS). We then sought to determine if the classifier could also distinguish insomnia

370

subtypes by training a classifier on our three different groups (GS vs. SSM vs. INS). Finally,

371

we examined if a classifier could distinguish between patients with CI using 2-class classifiers

372

trained on SSM and INS patients.

373

In addition to the number and definition of the groups to classify, we also varied the type of

374

data provided to the classifiers. The first type of classifiers (“visual” or “V” in Fig. 6) was

375

trained on features extracted from the hypnogram and thus obtained via a visual inspection of

376

PSG recordings (TST, total bed-time, sleep efficiency, WASO, duration and proportion of

377

time spent in each stage, micro-arousals, probability transition between stages, etc.). These

378

features correspond to the macroscopic features detailed above. The second type of classifiers

379

(“computed” or “C” in Fig. 6) was trained on features computed from the EEG recordings,

380

including meso (spectral features) and microstructural (slow waves, sleep spindles) variables.

381

Finally, we also trained classifiers on all features combined (“V+C” in Fig. 6).

382

Each classifier was trained and tested in the same way. We first split PSG recordings into a

383

training set (80% of the data) and a test set (remaining 20%). As there is 1 PSG recording per

384

patient, the training and test sets were disjoint groups of patients. The classifier was trained on

385

the training set using a Random Forest approach. A 3-fold cross-validation was performed to

386

select the classifier’s hyper-parameters. This procedure was repeated 10 times, each time with

387

a new random split. The model with the best hyper-parameters was then used to train the

388

classifier on the entire training set. Once the classifier was trained, we tested it on the test set,

389

which, importantly, was not used to train the classifier. We then compared the labels

390

predicted by the classifier with the real labels (e.g. INS, SSM or GS). The same methodology

391

was used for the classification of the EEG power spectrum per frequency bin (Fig. 2a, single-

392

feature classifiers) across the INS, SSM and GS groups.

393

Since the groups to predict are not equal, we computed either ROC (Receiver Operating

394

Characteristic) curve area (chance level: 0.5) or Cohen’s κ (chance level: 0) to quantify the

13

395

classifiers’ performance. A negative or null κ means that the classifier is not performing

396

above chance. The more the κ value is close to 1, the better the classifier is able to retrieve the

397

original labels of each individual recording. In order to better estimate the variability in the

398

classifiers’ ability to predict individuals’ clinical labels, we repeated this procedure 100 times

399

for each classifier. We plot in Fig. 6 the resulting distribution of κ values (N=100) as well as

400

the average confusion matrices over these 100 iterations. Finally, for each model we retrieved

401

the Variable Importance (VI) for each feature, which describes how important the feature is

402

for the classification. These VI values are shown for the top-20 features (i.e. the ones with the

403

highest VI values) for each model in Table 2.

404

Statistics

405

All statistical analyses were performed in Matlab. We examined group-effect (modulations

406

across the INS, SSM and control groups) using an Analysis of Covariance (ANCOVA). The

407

INS, SSM and control groups were used as a categorical predictor. We also included the age

408

and gender of participants as covariates. Bonferroni correction was used to account for

409

multiple comparisons. We used unpaired t-tests for post-hoc comparisons.

410

When comparing groups across time (Fig. 2), we relied on a cluster-permutation approach

411

[65]. For each time-point, we estimated the group-effect using an ANCOVA (covariates: age

412

and gender). We then identified clusters of time-points crossing a given uncorrected statistical

413

threshold (p<0.05). For each cluster, we computed the sum of the F-values associated with the

414

group-effect, for all the samples within the cluster. A surrogate dataset was obtained by

415

repeating this procedure when permuting the group labels across subjects (1000

416

permutations). We then compared the cluster statistics (sum of F-values) of the original

417

dataset with the maximum cluster statistics of the 1000 random permutations. The position of

418

the cluster statistics within the surrogate dataset was used to estimate the significance of the

419

cluster, allowing us to compute a nonparametric Monte-Carlo p-value (pcluster) for that

420

particular cluster.

421

Ethics

422

This research was conducted according to the principles expressed in the Declaration of

423

Helsinki of 1975, revised in 2001. The retrospective analysis of PSG data of patients and

424

controls was approved by the local ethics committee (Comité de Protection des personnes

425

(CPP) Ouest IV, from Nantes, France). Patients data were anonymously analyzed according

14

426

to legal requirements (Commission Informatique et Liberté (CNIL) from the French

427

Government).

15

428

RESULTS

429

We investigated how PSG recordings differ between the three groups of sleepers: patients

430

diagnosed with insomnia but without SSM (INS group), patients with insomnia and SSM

431

(SSM group) and Good Sleepers (GS group). As previously explained in the Methods section,

432

we extracted various features from PSG recordings. First, we extracted classical summary

433

statistics derived from the hypnograms obtained after visual scoring of the PSG (which we

434

termed “macroscopic” features); then features extracted from the spectral decomposition of

435

the EEG data (“mesoscopic” features) to capture changes in sleep/wake oscillations across the

436

three groups; finally, key graphoelements characteristic of either NREM (slow waves, sleep

437

spindles) and REM (eye-movements) sleep were compared across groups (“microscopic”

438

features). The macro-, meso-, and microscopic features were then aggregated and used to

439

train a supervised ML algorithm to classify patients in the three groups.

440

Hypnogram-based macroscopic features confirm the sub-phenotyping of insomnia

441

patients with and without SSM

442

When first comparing hypnograms of sleepers using classical metrics of sleep quantity and

443

quality (Table 1), we confirmed that patients diagnosed with insomnia but without SSM spent

444

less time asleep than good sleepers (TST: 361 min ± 5.1 (mean ± SEM) vs. 406 min ± 5.4;

445

unpaired t-test: p=4.0.10-6) and had a decreased sleep efficiency (72.0% ± 0.8 vs. 79.7% ±

446

1.1; unpaired t-test: p=2.1.10-6). Even when taking into account differences in total sleep time,

447

patients from the INS group spent a larger proportion of time awake during the night (27.8%

448

± 0.8 vs. 20.3% ± 1.1; unpaired t-test: p=4.1.10-6) and less time in NREM2 (41.7% ± 0.7 vs.

449

47.1% ± 0.9; unpaired t-test: p=3.8.10-5) as well as REM sleep (12.9% ± 0.3 vs. 15.0% ± 0.5;

450

unpaired t-test: p=0.001). The reduction in NREM2 was observed throughout the night (Fig.

451

1, top; unpaired t-tests: p<0.005 for both 1st and 2nd halves of the night) whereas the

452

decrease in the proportion of REM sleep was observed only in the end of the night (p=0.003).

453

These results confirm the rather archetypal profile of the patients from the INS group.

454

However, the same features failed to evidence any sleep disturbance in the insomnia group

455

with SSM. These patients appeared to actually sleep better than controls, with increased sleep

456

efficiency (88.4% ± 0.8 vs. 79.7% ± 1.1; unpaired t-test: p=2.4.10-8), decreased proportion of

457

wakefulness during the night (11.7% ± 0.8 vs. 20.3% ± 1.1; unpaired t-test: p=2.2.10-8) and

458

increased proportion of NREM3 stage (19.5% ± 1.2 vs. 15.1% ± 0.7; unpaired t-test:

459

p=4.4.10-4). Yet, based on subjective complaints, these patients were diagnosed with

16

460

insomnia. Contrary to previous reports, both INS and SSM patients did not show a significant

461

increase in the density of arousals in REM or NREM sleep when correcting for multiple

462

comparisons (Table 1).

463

We further examined the dynamics of individual nights. Using ANCOVAs, we examined the

464

interactions between the part of the night (1st vs. 2nd half) and individuals’ group and found a

465

significant interaction only for NREM3 (Bonferroni correction, p=8.45.10-7). This interaction

466

seems driven by INS patients who show less NREM3 in the beginning of the night compared

467

to controls and SSM patients but more NREM3 in the second part of the night (Fig. 1a). There

468

was no other significant interaction in other sleep stages (after Bonferroni correction)

469

suggesting that other group-effects are stable throughout the night.

470

We also examined how sleepers dynamically transit from one sleep stage to another by

471

looking at the transition probabilities derived from each hypnogram (Fig. 1, bottom; see also

472

Methods). There was a significant difference in how sleepers transit from stage NREM2 and

473

wakefulness (ANCOVA: p<10-6). The effect on the transition from NREM2 seemed mostly

474

driven by the INS group showing an unstable NREM2 stage (less NREM2-to-NREM2

475

transitions compared to controls: unpaired t-test p=2.3.10-5). The SSM group differed from

476

both INS and control groups by the tendency of these individuals to transit more from

477

wakefulness to NREM2 (wake-to-NREM2 transition compared to controls: p=5.2.10-8). These

478

rapid and frequent transitions to deeper stages of NREM sleep illustrate well the SSM

479

paradox, whereby subjective complaints contrast with a better-than-usual transition to sleep.

480

Features derived from the EEG spectrum show common differences between INS and

481

SSM patients compared to good sleepers

482

In this first level of analysis, examining macroscopic features extracted from the hypnograms

483

show that individuals from the SSM group were much more similar to good sleepers than

484

other insomnia patients (INS group). Yet, based on subjective complaints, SSM patients were

485

diagnosed with CI. To overcome this paradox, we set out to analyze in more depth PSG

486

recordings. First, we computed and examine the spectral decomposition of the EEG signal on

487

30-s-long windows (Fig. 2). We termed the output of this spectral decomposition mesoscopic

488

features as it represents a level of analysis between hypnogram-based macroscopic features

489

(summarizing an entire night) and sleep micro-structure (spindles, slow waves; time scale of

490

~1s).

17

491

To examine whether the power in certain frequencies could distinguish across clinical groups,

492

we used a supervised classifier approach. We trained a classifier on a subset of data and on a

493

single feature (relative power for a given frequency in REM or NREM sleep) to categorize

494

patients into our three clinical groups (INS, SSM and controls; see Methods for details). We

495

then tested the trained classifier on the remaining data, not included in the training set. The

496

performance of the classifier (quantified as the ROC area, see Methods) was then used as a

497

proxy to determine to what extent a given frequency band can discriminate individuals across

498

clinical groups. Values higher than 0.5 denote above-chance performance. Importantly, this

499

approach does not rely on the a priori definition of frequency bands of interest.

500

The results indicate that in both NREM and REM sleep, individual recordings can be

501

accurately categorized in the 3 clinical groups across all frequencies (Fig. 2a; NREM and

502

REM clusters: [0.5-20]Hz, pcluster<0.001). However, the decoding accuracy was not constant

503

and certain frequency domains led to particularly high classifying accuracies (peaks in

504

classifying performance). In particular, we could identify 4 main clinically relevant frequency

505

bands: (i) delta ([1-4]Hz), (ii) theta ([5-7]Hz), (iv) sigma oscillations ([11-15]Hz) and (iii)

506

beta oscillations ([16-20]Hz). Training and testing our classifiers on NREM and REM sleep

507

separately showed that certain frequency ranges give higher classifying accuracies for a given

508

sleep state. For example, theta/alpha (alpha: [8-11]Hz) and beta oscillations led to higher

509

decoding accuracy in REM sleep than in NREM sleep (clusters: [5, 10.5]Hz and [17.5-20]Hz,

510

pcluster<0.005) whereas in NREM sleep, delta and sigma oscillations led to higher decoding

511

accuracies (clusters: [0.5, 4.5]Hz and [11.5, 14.5]Hz, pcluster<0.0001). These results are

512

concordant with the dominant rhythms in NREM (slow waves: delta, sleep spindles: sigma)

513

and REM sleep (theta and fast oscillations). It is finally worth noting the high levels of

514

accuracy obtained for both NREM and REM within the delta range (ROC area > 0.8), which

515

suggests that simple features extracted from the EEG spectrum can accurately support the

516

automated identification of insomnia.

517

Then, as detailed in the Methods, we examined how the three different groups differed in each

518

of the frequency bands of interest (delta, theta/alpha, sigma and beta) and throughout the

519

night. This analysis was motivated by the well-documented influence of both circadian and

520

homeostatic processes on sleep rhythms [66]. Besides, past research has stressed that

521

differences between good sleepers and individuals suffering from insomnia also vary during

522

the night [30]. Differences were observed in the delta ([0.5, 4]Hz), theta/alpha ([5, 11]Hz),

523

sigma ([11, 15]Hz) and beta ([16, 20]Hz) bands (Bonferroni correction for cluster Monte18

524

Carlo p-values) but were restricted to the beginning of the night (<50% total progression).

525

Good sleepers were characterized by higher levels of delta power but lower levels of

526

theta/alpha and beta oscillations. In other words, insomnia patients showed weaker sleep-

527

related oscillations and stronger wake-related oscillations during sleep than good sleepers. We

528

observed however an exception to that rule with the small, temporally restricted, modulation

529

in spindle power (cluster: [12, 16]%, pcluster<0.05, corrected), with controls showing less

530

spindle power than insomnia patients.

531

Importantly, despite drastic differences at the macroscopic level, the spectrum of the insomnia

532

subtypes (INS and SSM) significantly overlap. Only one significant post-hoc cluster emerged

533

when comparing INS and SSM, with INS patients showing an increased level of alpha/theta

534

oscillations compared to SSM (post-hoc cluster: [0, 30]%, pcluster<0.005). These results alone

535

could positively help overcome the discrepancy between objective and subjective assessments

536

of insomnia: SSM patients no longer look like good sleepers but are much more similar to

537

INS patients when examining the spectral features of the EEG signal.

538

While quite standard, these spectral analyses have several limitations. First, since they rely on

539

relative power, it is difficult to be conclusive on the exact frequencies impacted by insomnia.

540

Indeed, effects observed on a specific frequency band could actually stem from other

541

frequency domains used in the normalization. Second, while frequency bands have been often

542

linked to specific patterns of neuronal activity (e.g. sleep spindles and the sigma band), this

543

relationship can be complex. For example, an increase in sigma power does not necessarily

544

mean an increase in spindle density as sleep spindles are also defined by a duration criterion.

545

Third, there can be significant inter-individual variations in terms of the frequency of the

546

different sleep rhythms, which are not taken into account when dealing with fixed frequency

547

bands. Finally, the EEG power spectrum does not only reflect periodic brain oscillations but

548

also aperiodic activity [61,62]. Periodic oscillations typically show up as peaks in the power

549

spectrum whereas aperiodic signals can be quantified by computing the slope of the 1/f trend

550

of the EEG spectrum. This is why we also examined whether insomnia had an effect on the

551

aperiodic component of the EEG spectrum by computing the slope of the power spectrum

552

across all sleep stages and for each individual (Fig. 3, see Methods). Bonferroni-corrected

553

ANCOVAs show significant modulations of the spectrum’s slope in NREM2 and NREM3

554

(p<0.01, Bonferroni corrected). This modulation was driven by decreased slopes for patients

555

with insomnia (INS and SSM) compared to controls.

19

556

The decrease in the slope of the spectrum was even more pronounced for SSM patients in

557

NREM3, which showed slopes that were smaller than in NREM2 and equivalent to NREM1,

558

in striking contrast with both controls and INS patients. Indeed, in controls and INS patients,

559

the slope increases with sleep depth (wake
560

in the slope of the EEG spectrum for SSM patients is particularly interesting since the slope

561

of the power spectrum has been associated with the excitation/inhibition balance [62]. A

562

decrease in the slope of the spectrum during sleep could be associated with an abnormally

563

high level of excitation during NREM3 in SSM patients, in link with the hyperarousal model

564

of insomnia (see Discussion).

565

Sleep’s micro-structure evidences a decrease in slow waves and increase in sleep

566

spindles in insomnia

567

Finally, as detailed in the Methods, we focused on the micro-structure of sleep and NREM

568

sleep hallmarks such as slow waves (including K-complexes) and sleep spindles. We

569

automated the detection of slow waves and extracted slow-waves’ density (number of events

570

per minute), amplitude, frequency and slope (see Fig. 4 and Methods). We used Bonferroni-

571

corrected ANCOVAs to assess the differences across groups in NREM2 and NREM3

572

separately, as well as for central and frontal derivations separately (16 comparisons across all

573

slow waves variables). Results indicate that, in both NREM2 and NREM3, for central

574

electrodes, insomnia patients showed fewer slow waves (ANCOVAs: p<10-6; post-hoc

575

unpaired t-tests p<0.001) as well as slower and less steep slow waves (ANCOVAs: p<10-4;

576

post-hoc unpaired t-tests p<0.001). The reduction of slow waves frequency was also observed

577

in frontal electrodes in NREM2 and NREM3 (ANCOVAs: p<0.001). The amplitude of slow

578

waves was not significantly modulated across groups when correcting for multiple

579

comparisons. When comparing SSM and INS patients directly, there was no significant

580

difference between the density and properties of slow waves in NREM sleep (uncorrected

581

post-hoc unpaired t-tests: all p>0.05 except for a minor difference in slow-wave amplitude:

582

p=0.046 in NREM2) suggesting that the impairment of slow-wave generation is a common

583

feature of insomnia subtypes.

584

We then examined sleep spindles, yet again based on automated detection algorithms (see

585

Methods). Bonferroni ANCOVAs were used to assess group differences in terms of spindles

586

density, maximum amplitude, duration and frequency over central and frontal derivations and

587

in NREM2 and NREM3 separately (16 comparisons). As for slow waves, spindle density

20

588

appears modulated across groups, this time in both NREM2 and NREM3, frontal and central

589

derivations (ANCOVAs, all p<0.001, corrected threshold). However, the direction of this

590

modulation was opposite to slow waves, with INS and SSM patients showing more sleep

591

spindles than controls (post hoc t-tests: P<0.001). These spindles also tended to vary in

592

duration in NREM2 and NREM3, central and frontal electrodes (ANCOVAs, all p<0.001,

593

corrected threshold). Similarly, spindle power was reduced in insomnia patients for central

594

and frontal derivations, NREM2 and NREM3 (ANCOVAs, all p<0.001, corrected threshold).

595

Spindle frequency varied across groups in NREM2 only (ANCOVAs, all p<0.05, corrected

596

threshold) with faster spindles in INS and SSM patients.

597

Importantly, there were significant differences between insomnia subtypes in terms of spindle

598

density. SSM patients showed larger number of sleep spindles compared to INS patients, in

599

NREM2 and NREM3, for central and frontal derivations (uncorrected post-hoc unpaired t-

600

tests: p<0.005).

601

Unsupervised AI algorithms can detect and phenotype insomnia using features

602

extracted from PSG recordings

603

From the results presented above, it appears that PSG recordings contain information

604

allowing not only to distinguish insomnia patients from good sleepers but can also make

605

important distinctions within insomnia (e.g. between insomnia with and without SSM).

606

However, the previous analyses are rarely employed in clinical settings, potentially because

607

they are complex, varied and often arbitrarily focus on specific aspects of the data (e.g. power

608

spectrum or micro-structure).

609

To facilitate the translation of our approach to clinical settings, we set out to determine if

610

features extracted from PSG recordings could predict whether an individual is a good sleeper

611

or suffers from insomnia. To do so, we relied on recent developments in computer science

612

and machine learning, which have made the computation and classification of large datasets

613

seamless (see Methods). In practice, for each PSG recording and patient, we extracted the

614

different macro, meso and micro-features described above. These features were divided into

615

“visual” (typically micro) and “computed” (typically meso and macro) features, depending on

616

whether they were obtained through the visual inspection of the data (N=71 features) or

617

through automated algorithms (N=302). This separation of visual and computed features was

618

made in order to check whether an entirely automated analysis of the signal would be enough

21

619

to help the diagnosis of insomnia and whether it could even outperform a classification based

620

on features obtained from the visual inspection of the PSG recordings.

621

We first focused on whether a classifier could predict if an individual suffers from CI by

622

training a classifier on good sleepers (GS) and patients with CI (SSM + INS) (see Methods).

623

To assess the efficacy of the classifier, we computed Cohen’s κ values for 100 repetitions of

624

this classification (Fig. 6a, left). For memory, a perfect classification corresponds to κ=1

625

(irrespective of the number of predicted classes) while a random classification corresponds to

626

κ=0. We also show the Confusion Matrix which represents how the predicted labels (rows)

627

compared to the real labels (columns). A perfect classification shows up as a diagonal

628

confusion matrix (predicted labels = real labels). Our results indicate that participants’ clinical

629

assessment (GS or CI) can be recovered with moderate accuracy when using visual features

630

(κ=0.49 ± 0.01) but this accuracy is much improved when using computed features (κ=0.88 ±

631

0.01; unpaired t-test: p<10-16). In fact, κ values above 0.8 are usually considered as almost

632

perfect classification [67]. Combining visual and computed features did not significantly

633

improve the overall accuracy of the classifier (κ=0.88 ± 0.007; unpaired t-test to compare

634

visual+computed and computed classifiers: p=0.67), implying that computed features already

635

maximize the ability to predict CI from PSG recordings.

636

We then examined whether we could also retrieve insomnia subtype. To do so, we trained a

637

classifier on our three clinical groups (GS, SSM and INS, Fig. 6b). Visual features led to

638

moderate accuracy (κ=0.45 ± 0.009). As the confusion matrix shows, this moderate level of

639

performance is due to a large proportion of good sleepers (56.8%) and SSM patients (46.5%)

640

being mistakenly diagnosed as INS patients. A limited ability to classify these 3 groups is

641

expected given that SSM, INS and GS individuals sometimes overlap when considering only

642

visual (micro) features (see Fig. 1 and Table 1). However, the performance significantly

643

improved when focusing on features extracted from the EEG signal (κ=0.59 ± 0.008; unpaired

644

t-test with the visual-features classifier: p<10-16). In this case, GS were reasonably well

645

categorized (87.5% correct) but SSM patients were almost always categorized as INS patients

646

(96.1% miscategorization as INS patients). This is again not surprising when considering that

647

INS and SSM patients largely overlap when considering the computed features: meso (Fig. 2-

648

3) and micro (Fig. 4-5) features. Finally, combining both sets of features led to significantly

649

better performance (κ=0.73 ± 0.007; unpaired t-test compared to visual- or computed-features

650

classifiers: p<10-16),

22

651

Given the inability for the 3-way classifier to distinguish between INS and SSM patients, we

652

finally trained and tested classifiers when using INS and SSM these patients only. Using

653

visual features, we could classify the two subtypes of insomnia with moderate accuracy

654

(κ=0.51 ± 0.01). But, when considering features extracted from the EEG signal, the classifier

655

was at chance (κ=0.0049 ± 0.008; t-test compared to 0: p=0.51). A significantly better-than-

656

chance classification was retrieved when combining both visual and computed features

657

(κ=0.54 ± 0.02; t-test compared to 0: p<10-16), but the level of performance attained was not

658

significantly different from the visual-only classifier (unpaired t-test: p=0.07). These results

659

confirm that SSM and INS patients largely overlapped for EEG-derived features, making

660

them indistinguishable by an automated classifier.

661

Finally, supervised classifiers have the crucial advantage of providing insight on the nature of

662

group differences. Indeed, the ML algorithms used here allowed us to retrieve which features

663

contributed the most to the classification output, which can be interpreted (when the classifier

664

is performing well) as the features that best distinguish the groups of interest (see Methods).

665

By stressing which differences matter, supervised classifiers could shed light on the

666

physiological substrates and aetiology of insomnia. To illustrate this possibility, we show in

667

Table 2 the top-20 most discriminative features for the GS. vs. CI, GS vs. SSM vs. INS, and

668

SSM vs. INS classifiers. As expected from Fig. 2, spectral information in the delta band is

669

discriminating best between GS and CI patients (top-20 features were all computed from the

670

power spectrum). When classifying GS, SSM and INS patients however, the top features were

671

a combination of features extracted from the EEG power spectrum and the hypnogram. This

672

may be related to the fact that SSM and INS patients do not seem to differ in terms of power

673

spectrum but do differ in terms of hypnograms. In fact, it seems that only the combination of

674

the information coming from the micro, meso and macro levels can allow the 3-way

675

distinction of the INS, SSM and GS groups. This was illustrated by the classifier focusing on

676

the SSM and INS groups. In this case, the top-20 variables were a combination of

677

hypnogram-based features, spectral features and information about sleep micro-structure

678

(sleep spindles).

679

Impact of covariates and limitations

680

This study being a retrospective analysis, it bears several potential limiting factors associated

681

with the nature of the dataset and the individuals included in this study. Indeed, the different

682

groups are not age or gender-matched: good sleepers were on average younger and comprised

23

683

a greater proportion of males (Table 1). Age and gender impacting PSG recordings at the

684

macro, meso and micro levels [68–70], we integrated these two variables as covariates in all

685

our analyses (see Statistics section).

686

Another possible confound is the fact that patients were not instructed to interrupt their

687

treatments and, consequently, 52% of INS and 53% of SSM patients were under insomnia

688

medication during the night of PSG, which is known to affect the recordings [71]. To mitigate

689

the risk that part of our results could result from the effect of medications, we replicated all

690

our analyses by discarding patients with any record of medication. By and large, these

691

replications confirm our initial results regarding the meso and micro level of analysis. Indeed,

692

we could confirm that unmedicated insomnia patients show a reduction in slow wave density

693

(uncorrected post-hoc t-tests: p<0.01 in NREM2/3, central electrodes), frequency (p<0.01 in

694

NREM2/3, central electrodes) and slope (p<0.05 in NREM2/3, central electrodes); an

695

increase in spindle density (p<0.001 in central/frontal channels and NREM2/3), frequency

696

(p<0.01 in central channels and NREM2) and decrease in amplitude (p<0.001 in

697

central/frontal channels and NREM2/3); a decrease in delta oscillations coupled with an

698

increase in theta/alpha and beta oscillations (pcluster<0.05); a decrease in the slope of the power

699

spectrum in NREM2 and NREM3 (p<0.05). It seems thus that our results are confirmed when

700

taking into account confounding variables (age, gender and medication).

701

PSG recordings were also recorded in different locations, either at home (ambulatory

702

recordings) or at the hospital. The proportion of home recordings differed between controls

703

and patients (GS: 6.7%; INS: 71.2%; SSM: 80.2%) as most of patients were recorded at home

704

while controls consisted of volunteers to research protocols typically conducted in controlled

705

environments. The proportion of home recordings did not differ between insomnia sub-types

706

(chi-test: chi2 =2.4, p=0.12). Within the population of insomnia patients (INS + SSM), we

707

examined the impact of recordings’ location (home or hospital) on the macro, meso and micro

708

features. We observed an increase in Total Sleep Time, proportion of REM and number of

709

REM episodes for ambulatory recordings (p<0.01, Bonferroni-corrected unpaired t-tests,

710

N=273 ambulatory and 74 hospital recordings in 347 patients). This is due to the fact that

711

hospital recordings can be disrupted by hospital routine in the morning, leading to extended

712

and REM-rich home recordings. However, meso (spectral slope (Fig. 3) and overall power in

713

the delta, theta/alpha, sigma and beta bands (Fig. 2)) and micro (density and properties of

714

slow waves (Fig. 4) and sleep spindles (Fig. 5)) features were not significantly impacted by

715

the recording type within the insomnia population (unpaired t-tests, corrected threshold: 24

716

0.05). Furthermore, when comparing patients and controls, most of the effects reported

717

concern NREM sleep (Fig. 3-5) or the beginning of the night (Fig. 2) and are thus less likely

718

to be affected by an extension of sleep in ambulatory recordings.

719

Finally, PSGs were recorded using different devices. Although all these devices are routinely

720

and indiscriminately used in our Sleep Clinic for clinical assessments, different devices could

721

alter some aspects of the PSG recordings. In addition, the type of recording device differed

722

between controls (82% of ACTIWAVE devices) and insomnia patients (99% and 100% of

723

NOX-A1 devices for INS and SSM patients resp.). However, most of the differences

724

observed between patients and controls were limited to specific sleep stages (Fig. 1-5), time

725

windows (Fig. 1, 2b), scalp location (Fig. 4-5) or to specific properties of the micro-structure

726

(Fig. 4-5). It is thus unlikely that the recording device used in a given PSG would be the

727

source of the observed group differences as the type of recording device should affect a

728

specific feature of PSG recordings indiscriminately of the time, sleep stage or location of the

729

recorded data.

730

25

731

DISCUSSION

732

Towards a comprehensive exploration of the physiological substrates of insomnia

733

Our study revisits the use of PSG in the diagnosis of insomnia. Indeed, insomnia stands in

734

striking contrast with other sleep pathologies as PSG recordings are neither needed nor

735

recommended for the diagnosis of insomnia, despite the fact that PSG is widely considered as

736

the golden standard for the objective measurement of sleep [19]. This paradox has been

737

justified by the fact that PSG recordings do not bring reliable information to inform the

738

diagnosis and treatment of insomnia. PSG recordings can even, at times, directly contradict

739

what individuals report [27]. A telltale example of such discrepancies is the case of insomnia

740

with SSM, in which patients can sometimes be indistinguishable from good sleepers when

741

referring to classical summary statistics of PSG recordings (TSA, WASO, number of

742

awakenings, time spent in each sleep sub-stage) [28,29]. In this study, we set out to resolve

743

this discrepancy between objective and subjective indexes of sleep and to reconcile insomnia

744

with PSG recordings. To do so, we relied on a large dataset of PSG recordings in insomnia

745

patients (N=347), including different insomnia subtypes (N=59 with SSM, N=288 without).

746

The size of this dataset allowed us to examine PSG recordings at various scales of analyses

747

and to apply Machine Learning (ML) techniques, typically requiring large datasets.

748

Our approach was to systematically perform and aggregate the different types of analyses

749

performed in the past to investigate insomnia in PSG recordings. We divided these analyses

750

into 3 levels. First, we extracted “macroscopic” indexes of sleep quantity and quality as

751

classically reported in the literature on insomnia (TST, WASO, Sleep Efficiency, proportions

752

of Sleep Stages, etc). These metrics tend to summarize a given aspect of sleep across the

753

entire night. Second, we focused on the EEG signal on 30-s-long epochs and analyzed brain

754

rhythms through the spectral decomposition of the EEG signal (see [30–33] and [18] for a

755

review on similar approaches in insomnia). We termed this level of analysis “mesoscopic” as

756

it focuses on an intermediate time-scale (30s). Third, we performed a fine-grained analysis

757

(~1s scale) of PSG’s microstructure (slow waves and sleep spindles). Past studies have

758

investigated changes in sleep microstructure in insomnia (see [18] for a review) but these

759

results remained largely inconclusive. Although most of the analyses presented here have a

760

precedent in the literature, our study is the first to integrate all these approaches.

761

26

762

PSG as a useful tool for the diagnosis of insomnia

763

The analysis of the macro features of PSG recordings illustrate the distinctions classically

764

made between insomnia with and without SSM. Indeed, global metrics of sleep quantity and

765

quality derived from the hypnogram show two groups of insomnia patients: individuals in

766

which PSG recordings tend to confirm the subjective complaint of insomnia (CI without

767

SSM: INS group) and individuals in which PSG recordings do not support or even appear to

768

contradict the diagnosis of CI (CI with SSM: SSM group). In the latter case, patients appeared

769

to sleep as well if not better than controls (equivalent TTS; shorter SOL, WASO; better sleep

770

efficiency; see Table 1). In contrast, patients without SSM had shorter TTS, longer WASO

771

and worse sleep efficiency (Table 1). It is worth noting the rather long SOL observed in

772

controls, which could be explained by a FNE [22]. A reverse FNE could be also responsible

773

for the short SOL observed in patients with SSM [23].

774

While macro features of sleep are an easy way to rapidly assess a night, our analyses and

775

previous studies show their limitations as they fail to capture a common denominator among

776

insomnia patients. Examining further the proportion of sleep stages leads to the same

777

conclusion: patients without SSM showed a deteriorated sleep (increased proportion of wake

778

during the night, decreased proportion of NREM2 and REM; Table 1) whereas patients with

779

SSM displayed the reverse pattern (less wake, more NREM2, NREM3 and REM; Table 1).

780

Similar results were obtained when splitting the night into two parts (Fig. 1, top).

781

Interestingly, the increase in REM for SSM patients could be interpreted in the light of the

782

positive relationship between REM duration and subjective estimates of the time spent awake

783

during the night [35]. The fact that REM sleep is often associated with dreaming [72], and

784

therefore consciousness, could potentially explain the difficulty experienced by patients in

785

determining if they were awake or asleep. However, we did not replicate previous findings on

786

the increase in arousals during sleep, and REM in particular, in insomnia [35]. Nonetheless,

787

we could observe a trend in this direction and our results could be explained by the strict

788

statistical thresholds used in this study (Bonferroni correction, see Methods). Finally, the

789

analysis of state-transitions during the night (i.e. how sleepers move from one state to

790

another; Fig. 1, bottom) also describes INS patients’ sleep as unstable (more frequent

791

NREM2>Wake transitions compared to controls) and SSM patients’ sleep as more stable

792

(more frequent WAKE>NREM2 transitions compared to controls).

27

793

Overall macro features derived from the hypnogram do not seem able to solve the conundrum

794

of insomnia as they fail at unravelling a common denominator between insomnia subtypes. Of

795

course, this does not mean that PSG recordings are not informative, even at this level of

796

analysis. Rather, information extracted from PSG recordings allows the phenotyping of

797

insomnia (e.g. to determine the presence of SSM) based on a pre-existing diagnosis of

798

insomnia established on patients’ subjective complaint.

799

Nonetheless, there is more in PSG recordings than what can be derived from the hypnogram

800

and previous studies have shown how PSG recordings can be harnessed to better understand

801

the physiopathology of insomnia. In particular, the spectral decomposition of the EEG signal

802

allows identifying and quantifying the neural oscillations that are constitutive of wakefulness

803

and sleep. Accordingly, we here extracted spectral information on epochs of 30s (mesoscopic

804

features; see Methods) and examined the relative power across the entire [0.5-20]Hz range

805

(Fig. 2a) or in frequency-bands of interest (Fig. 2b). Past studies have shown that individuals

806

with insomnia present more fast oscillations that are characteristic of wakefulness during

807

sleep (alpha, beta oscillations) and less oscillations typical of sleep (delta rhythm) (see [18,30]

808

for reviews). Our results are in line with these findings (Fig. 3). However, the sigma range,

809

which is typically associated with sleep, showed a moderate up-regulation in insomnia

810

patients compared to controls. Such an increase seems driven by SSM patients, which is

811

concordant with previous findings in “subjective” insomnia [73]. This increase in sigma

812

power in CI patients speaks against a simple down-regulation of sleep rhythms and up-

813

regulation of wake activity during sleep in CI.

814

A striking aspect of the comparison between macro and meso features is that, while macro

815

features clearly distinguished between insomnia sub-types (i.e. SSM patients are closer to

816

good sleepers than INS patients according to these metrics), the meso features did not (Fig.

817

3). Both SSM and INS patients showed very similar changes in power compared to controls,

818

demonstrating that the paradox of SSM dissolves as soon as the neural activity is analyzed in

819

more detail. This is an important result in order to reassess the validity of PSG recordings in

820

insomnia as it shows that, despite inconsistencies at the level of macroscopic metrics, PSG

821

recordings can actually show robust markers of insomnia. Finally, although we observed

822

robust differences across groups in several frequency bands, the fact that we could only

823

examine the EEG spectrum in a limited number of scalp locations means that we cannot

824

conclude on the topographical aspects of these changes as previously done [34,74].

825

Furthermore, we could not explore other spectral differences such as differences in occipital 28

826

alpha or sigma [34]. The replication of our findings in high-density EEG could lead to further

827

insights about insomnia patients and good sleepers differ.

828

Examining the EEG spectrum, we also extracted information about the aperiodic component

829

of neural activity. Indeed, the EEG signal is not uniquely composed of oscillations or pseudo-

830

oscillations [54]. Rather, neural oscillations are embedded in an aperiodic background, which

831

translates into the 1/f relationship between power and frequency observed in scalp EEG [61].

832

Importantly, modulations of this aperiodic background are physiologically and cognitively

833

relevant as the parameters of this aperiodic activity can change with sleep/wake transitions

834

[64], age [75] or can be altered in disorders such as schizophrenia [76]. Here, we show that

835

the slope of the aperiodic signal is decreased in CI patients (both SSM and INS) in deep

836

NREM sleep (NREM2 and NREM3, Fig. 3). Interestingly, a reduction of the slope of the

837

EEG spectrum has been interpreted as a modification of the Excitatory/Inhibitory (E/I)

838

balance among cortical networks and an increase in cortical excitability [62]. If the latter

839

findings can be extrapolated to humans, our results would suggest that insomnia is

840

characterized by an increase in cortical excitability (see below).

841

Finally, we dove further into the details of sleep physiology and focused on sleep’s

842

microstructure (micro features). We automatically detected slow waves and sleep spindles

843

(see Methods) and extracted basic properties of individual grapholelements (frequency,

844

duration, amplitude, etc). In concordance with the spectral analyses (e.g. decrease in delta

845

power, Fig. 2), we observed a decrease in slow-wave density in both insomnia groups (Fig.

846

4). Slow waves were not only less numerous in insomnia but they also appeared slower

847

(decreased frequency) and with reduced down-to-up slopes. Previous reports indicate that

848

slow waves with reduced slopes tend to occur later during the night [55,77] and recruit

849

smaller portions of the cortex [58,77]. Less steep, slower slow waves have also been

850

interpreted as reflecting a smaller recruitment of neuronal populations due to reduced synaptic

851

strength or density [78] but changes in wake-promoting neuromodulation or inhibitory

852

neurons could also be responsible [77,78]. In addition, slow waves are usually steeper

853

following sleep deprivation [55]. Consequently, less steep slow waves in a population that

854

complaint from chronic sleep loss could be interpreted as a disruption of sleep’s homeostatic

855

regulations. Finally, the changes observed in slow waves could affect the physiological

856

functions of slow waves (e.g. synaptic homeostasis, memory consolidation [79–81]) and lead

857

to the subjective feeling of a non-recovering sleep as well as daytime cognitive impairments

858

as typically seen in insomnia. 29

859

The analysis of the microstructure also showed that, contrary to slow waves, sleep spindles

860

are increased in both insomnia groups compared to GS (Fig. 5), with a maximal increase for

861

SSM patients. This increase in spindle density is concordant with the increase in sigma power

862

observed in the EEG spectrum (Fig. 3b). Sleep spindles have also reduced amplitude, and

863

increased frequency and duration in insomnia patients (Fig. 5). This increase in spindle

864

density could seem at odds with our findings which indicate a reduction in sleep rhythms in

865

favor of wake rhythms in insomnia patients (Fig. 3). However, an increase in spindle

866

frequency has been observed under conditions of circadian misalignment [82], while the

867

administration of benzodiazepine [83] or melatonin [84] tends to decrease spindle frequency,

868

suggesting that an increase in spindle frequency is a marker of a deteriorated or lighter form

869

of NREM sleep. Compatible with this view is the negative relationship between spindle

870

frequency and slow-wave activity (a classical marker of sleep depth and quality) that has been

871

previously reported [57].

872 873

PSG recording can inform on the mechanisms underlying insomnia with or without SSM

874

Several models have tried to provide a neurophysiological understanding of insomnia with the

875

hyperarousal model of insomnia [17,85] certainly being the most influential. This model

876

posits that insomnia is rooted in neurophysiology rather than being a purely psychological

877

disorder. Although this model recognizes the role of psychological components in the onset

878

and maintenance of insomnia, it views these components as being potentiated by a

879

physiological substrate. According to this model, a higher level of somatic, cognitive and

880

cortical activity (or hyperarousal) [17,73,74] would be such substrate. Dysregulations of

881

neuroendocrine systems (such as the hypothalamic-pituitary-adrenal-axis responsible for the

882

production of cortisol) have been recently proposed as a cause of this hyperarousal [86].

883

Certain changes in sleep physiology observed in insomnia support the hyperarousal model

884

such as the increase in high-frequency rhythms observed in insomnia patients with or without

885

SSM [17]. A recent study relying on high-density EEG showed that even during deep sleep,

886

individuals with CI show an increase in the prevalence of neuronal patterns of brain activity

887

typically associated with light sleep [36], a phenomenon that could be linked to hyperarousal.

888

Markers of hyperarousal have been also investigated in wakefulness since the model views

889

hyperarousal as a trait that would impact brain functioning day and night. Accordingly, it has

890

been shown that insomnia patients react more strongly to external sensory inputs and internal

891

stimuli (i.e. heartbeat) [87–90].

30

892

Although the hyperarousal model has gathered much supporting evidence, it is unclear if a

893

day-and-night increase in cortical arousal is really at the root of insomnia. Most of the studies

894

showing increase in fast oscillations during insomnia actually evidence stage-dependent

895

effects or effects constrained in specific parts of the night (typically the beginning)

896

[17,30,69]. Perlis and colleagues proposed a reformulation of the hyperarousal model

897

whereby insomnia is characterized by a disruption of homeostatic processes that promote the

898

transition to sleep at the beginning of the night [17,91]. Our results support this view as most

899

of the spectral differences between insomnia patients and controls were observed in the first

900

half of the night.

901

However, our results also indicate that insomnia was not simply characterized by an increase

902

in wake-like rhythms during sleep and a reduction in sleep-like oscillations. Indeed, we also

903

observed an increase in the number of sleep spindles in both insomnia groups (Fig. 5). This

904

increase cannot be simply explained by an increase in the proportion of NREM2 (where sleep

905

spindles are predominant [92]). Indeed, we observed an increase in sleep spindles in both

906

NREM2 and NREM3. Besides, the density of sleep spindles increased in the INS group

907

compared to controls (post-hoc unpaired t-tests: p<0.001 for frontal and central spindles)

908

despite a reduction in the proportion of NREM2 in INS patients (Table 1). Importantly,

909

spindle density was not the only parameter affected and we also observed an increase in the

910

frequency of sleep spindles (Fig. 5). Since the frequency and density of sleep spindles have

911

been found to be negatively correlated with Slow Wave Activity [57], the increase in spindle

912

density and spindle frequency observed in insomnia patients could be a direct consequence of

913

the reduction of slow waves, or both could be mediated by the same physiological

914

mechanism. We hypothesize here that both changes in slow waves (decrease density and

915

down-to-up slope) and sleep spindles (increased density and frequency) could be explained by

916

an increase in cortical excitability during sleep. Indeed, previous work in rodents has shown

917

that a down-regulation of cortical excitability with carbamazepine leads to increased slow-

918

wave power, steeper slow waves, slower spindles and fewer fast spindles [93]. In summary,

919

the administration of carbamazepine leads to a reverse profile compared to insomnia patients,

920

potentially due to the negative impact of carbamazepine on cortical excitability. In fact, the

921

administration of carbamazepine has also been shown to improve sleep parameters in

922

epileptic patients (see [94] for a review). This hypothesis is also supported by the analyses of

923

the spectral slope (see Fig. 3 and above). Thus, it is possible that an increase in cortical

31

924

excitability could be responsible for the physiological changes observed during sleep in

925

insomnia.

926

Machine Learning techniques can facilitate the integration of PSG recordings to clinical

927

practice

928

Our results show that fine-grained levels of analyses of PSG recordings can reveal robust

929

significant differences between insomnia patients and good sleepers but also between

930

insomnia subtypes. However, conducting these analyses can be time-consuming, impeding

931

their easy translation to clinical settings. Nonetheless, this translation might be facilitated by

932

two ongoing revolutions in the field of Sleep Medicine. The first one regards data and the fact

933

PSG devices have become smaller, lighter and cheaper with years. Recently, several large

934

datasets of PSG recordings have been published [68,95,96] or are in the making (e.g. Stanford

935

Technology Analytics and Genomics in Sleep (STAGES) Program), putting sleep research in

936

the era of “big data”. Most importantly, consumers-oriented devices now exist that could

937

bring PSG to the masses [42], facilitating the acquisition of massive amounts of sleep data. It

938

has thus become not only possible but also crucial to better understand what constitutes a

939

good night's sleep.

940

The second revolution is tied to the first one and regards data analysis. Indeed, PSG

941

recordings represent very rich data and, although existing methods allow to automatically

942

analyze multiple aspects of physiological data, recent developments in Artificial Intelligence

943

make it easier to integrate disparate analytic approaches. With the increase in the amount of

944

data available but also the type of available analyses, it is important to find new ways to

945

automatize and rationalize data analyses. Unfortunately, so far, studies focusing on PSG

946

recordings are confined to specific analytic approaches. Here we sought to find a way to

947

integrate these different approaches. We automated most of the analysis pipeline and showed

948

we could rely on supervised classifiers to extract information about individuals’ diagnosis.

949

Besides, even the steps that were here performed through visual inspection of the data (sleep

950

scoring) can now be reliably automated [95].

951

Supervised algorithms were here used in two ways. First, they were used as an exploratory

952

method to identify which features can distinguish between the groups of interest. For

953

example, applying supervised classifiers to spectral features (Fig. 3) allowed us to identify the

954

frequency domains that differ the most between good sleepers and insomnia patients (delta,

955

theta/alpha, sigma and beta oscillations) without relying on a priori hypotheses. The same

32

956

approach allowed us to investigate the differences between NREM and REM sleep,

957

evidencing that some neural oscillations are more predictive of insomnia in NREM (delta,

958

sigma) or REM (theta, beta) sleep.

959

Another use of supervised algorithms is to classify data according to clinically relevant

960

groups. We show here that different sets of features extracted from PSG recordings can either

961

succeed or fail at teasing apart good sleepers from insomnia patients, or to distinguishing

962

between insomnia subtypes (Fig. 6). For example, micro and meso features support an

963

excellent classification of insomnia patients vs. controls, in line with the notion that PSG

964

recordings can be highly relevant for the diagnosis of insomnia. In fact, our findings

965

demonstrate how an automatization of PSG analyses could be implemented to help clinicians

966

diagnose patients with a high level of accuracy (Fig. 6a, sensitivity > 98%, specificity >86%).

967

Importantly, classifiers are also informative when they fail. For example, the same features

968

that can distinguish insomnia patients from good sleepers (e.g. spectral information, micro-

969

structure), can fail to distinguish SSM and INS patients, which suggests that insomnia

970

subtypes are remarkably similar in terms of spectral components or micro-structure. This was

971

not the case for features extracted from the hypnogram. Hence this approach can confirm that,

972

although CI patients with or without insomnia differ according to macroscopic metrics of

973

sleep quantity and quality, their sleep is close to identical when looking at specific

974

physiological mechanisms.

975

Finally, another advantage of classifiers is that it is possible to examine which features are the

976

most responsible for the classifiers’ accuracy, which could provide insights about the

977

physiological substrate of insomnia. For example, the classification of CI patients vs. good

978

sleepers was based mostly on spectral information in the lower frequency range (delta/theta,

979

Table 2). This means that, when not taking into account the different types of insomnia, sleep-

980

related oscillations such as delta and theta rhythms represent a common signature for the

981

insomnia disorder. However, when factoring in insomnia subtypes, other features pertaining

982

to the micro and macro-scales become discriminative. These results stress the importance and

983

relevance of PSG recordings as they allow to identify both the core common physiological

984

signatures of insomnia as well as the components that are specific to particular insomnia

985

subtypes.

986

33

987

Conclusion:

988

We claim here that, in PSG recordings, there is more than meets the eye. PSG recordings

989

seem to carry very rich and reliable information about one’s sleep, in particular in the case of

990

insomnia. In fact, the paradox of SSM (presence of subjective symptoms of insomnia without

991

objective impairments of sleep) is apparent only when focusing on superficial, large-scale

992

metrics of sleep but dissolves when examining the finer dynamics of brain activity. We thus

993

conclude that the limitations outlined in the past and leading to the exclusion of PSG

994

recordings from the diagnosis of insomnia are no longer warranted and that clinicians should

995

revisit the benefits of PSG recordings.

996

We further argue that this reassessment is timely in the light of two ongoing revolutions in the

997

field of Sleep Medicine: (1) the emergence of consumer-based PSG devices will bring PSG

998

recordings to the masses, leading to an explosion in the amount of available data, similarly as

999

what happened with actigraphy, (2) the translation of computational tools from the field of

1000

Artificial Intelligence to Sleep Medicine allows the rapid, automated and massive analysis of

1001

large datasets and simplifies the difficult task of summarizing expanding amounts of data to

1002

human decision-makers (e.g. clinicians). We show here the feasibility of embracing such

1003

changes and their ability to facilitate the clinical diagnosis and management of a common but

1004

difficult sleep disorder: insomnia. We also demonstrate how these approaches can suggest

1005

new ways of understanding why some individuals sleep well while others struggle.

1006

34

1007

References:

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Figures and Tables: Table 1:

Age (years) Gender (% Females)

Controls (N=89)

SSM (N=59)

Insomnia (N=288)

Group Effect

34.5 ± 1.3

39.4 ± 1.5 (*) 76.2 (***) 399 ± 7.6 (ns) 20.5 ± 2.9 (***) 34.0 ± 3.6 (***) 88.3 ± 0.8 (***) 11.7 ± 0.8 (***) 1.7 ± 0.1

45.7 ± 0.8 (***) 68.6 (***) 361 ± 5.1 (***) 49.4 ± 3.1 (ns) 94.9 ± 4.2 (***) 72.0 ± 0.8 (***) 27.8 ± 0.8 (***) 2.0 ± 0.1

50.0 ± 1.1 (*) 19.5 ± 1.2 (***) 17.1 ± 0.7 (*) 4.1 ± 0.2

41.7 ± 0.7 (***) 15.3 ± 0.5 (ns) 12.9 ± 0.3 (**) 3.6 ± 0.1 3.1 ± 0.2 (***) 0.23 ± 0.01

1.5.10-10 (***) 1.5.10-22 (***) 3.2.10-5 (***) 5.2.10-6 (***) 1.5.10-10 (***) 9.5.10-16 (***) 2.8.10-15 (***) 0.73 (ns) 1.6.10-8 (***) 0.002 (ns) 2.8.10-6 (***) 0.03 (ns) 3.4.10-4 (**) 0.009 (ns) 0.006 (ns) 3.3.10-11 (***) 1.6.10-7 (***) 0.02 (ns)

21.3

TST (minutes)

406 ± 5.4

SOL (minutes)

50.3 ± 6.0

WASO (minutes)

57.1 ± 4.2

Sleep Efficiency (%)

79.7 ± 1.1

% Wake

20.3 ± 1.1

% N1

2.4 ± 0.2

% N2

47.1 ± 0.9

% N3

15.1 ± 0.7

% REM

15.0 ± 0.5

# Cycles

3.9 ± 0.1

# REM episodes

4.3 ± 0.3

Arousals NREM (/min)

0.20 ± 0.01

3.6 ± 0.2 (ns) 0.18 ± 0.01

Arousals REM (/min)

0.20 ± 0.01

0.24 ± 0.02

0.22 ± 0.01

Spindles (/min)

1.6 ± 0.1

Slow Waves (/min)

6.9 ± 0.4

REMS (/min)

6.4 ± 0.3

3.6 ± 0.3 (***) 4.6 ± 0.4 (***) 4.7 ± 0.3

2.7 ± 0.1 (***) 4.0 ± 0.2 (***) 5.1 ± 0.2

p-value, (corrected threshold)

1287

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1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332

Table 1: Demographics and sleep differences across controls (GS), SSM and INS patients. Numbers show the average and standard-error-of-the-mean (mean ± SEM) across individuals. The Group Effect column shows the p-values for an ANCOVA on groups with age and gender as covariates. In case of a significant group effect (Bonferroni correction for N=18 comparisons), the stars denote the corrected significance levels of post-hoc two-sided unpaired t-tests (GS vs. SSM and GS vs. INS resp.). ***: p<0.001; **: p<0.01; *:p<0.05; ns: p>0.5.

42

1333 1334

Table 2: Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

GS. vs. CI (VI) Power - [0-0.5] Hz - Wake (200) Power - [0.5-1] Hz - N2 (68.9) Power - [0.5-1] Hz - NREM (56.5) Power - ∂ - N2 (40.1) Power - ∂ - NREM (39.6) Power - [1.5-2] Hz - Wake (39.6) Power - [0-0.5] Hz - N2 (33.7) Power - [0-0.5] Hz - NREM (32.5) Power - [0.5-1] Hz - Wake (30.2) Power - [0-1.5] Hz – REM (29.1) Power - [1.5-2] Hz - N2 (27.4) Power - ∂ - Wake (27.3) Power - [1-1.5] Hz - NREM (26.2) Power - [1.5-2] Hz - NREM (25.6) Power - [0.5-1] Hz - REM (24.5) Power - [5.5-6] Hz - REM (24.1) Power - [1-1.5] Hz - N3 (23.8) Power - [6-6.5] Hz - REM (23.8) Power - [2-2.5] Hz - Wake (23.7) Power - [3.5-4] Hz - Wake (23.0)

GS. vs. SSM vs. INS (VI) Power - [0-0.5] Hz - Wake (99.5) Hypno - duration - Wake (46.2) Power - [0.5-1] Hz - N2 (42.4) Hypno - sleep efficiency (38.6) Hypno - % Wake (31.7) Power - [0.5-1] Hz - NREM (28.4)

SSM vs. INS (VI) Hypno - duration - Wake (196.8) Hypno - sleep efficiency (145.7) Hypno - % Wake (103.8) Hypno - transition - W>W (80.8) Hypno - WASO (69.7) Hypno - transition - W>N2 (63.8) Spindle – density C3/4 - N3 (47.0) Hypno - transition - N2>W (43.4) Spindle - duration C3/4 - N2 (42.1) Power - [10-10.5] Hz - N1 (40.9) Hypno - duration - REM (39.5)

Power - ∂ - N2 (26.3) Hypno - transition - W>W (25.9) Power - [1.5-2] Hz - Wake (23.6) Power - [0-0.5] Hz - N2 (21.1) Power - [0-0.5] Hz - NREM (20.8) Power - ∂ - NREM Hypno - sleep latency (38.2) (20.6) Hypno - WASO Hypno - duration - bed-time (20.6) (36.9) Power - [0.5-1] Hz - Wake Hypno - duration - sleep time (20.5) (36.0) Hypno - transition - W>N2 Hypno - transition - N3>N2 (20.3) (34.8) Power - [3.5-4] Hz - Wake Hypno - duration - cycle (19.7) (34.6) Power - [0.5-1] Hz - REM Hypno - arousals - N3 (18.9) (33.2) Power - ∂ - Wake Spindle - freq. F3/4 – N3 (18.6) (32.9) Power - [2-2.5] Hz - Wake Spindle - duration F3/4 – N3 (18.4) (32.0) Power - ß - Wake Power - [4 -4.5] Hz - REM (18.1) (31.4)

1335 1336

43

1337 1338 1339 1340 1341 1342 1343 1344

Table 2: Classifiers’ top-features. For each feature used by a classifier, a Variable Importance (VI) index can be calculated, which expresses the weight of this particular feature in the classification. We selected the top20 features for each classifier (1st column: 2-class GS vs. CI; 2nd column: 3-class GS vs. SSM vs. INS; 3rd column: 2-class SSM vs. INS) and show the name of these features sorted in descending order of VI value. For each feature, the VI value is also displayed (parentheses).

44

1345 1346

1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366

Figure 1:

Figure 1: Sleep stages and stages-transitions across controls, SSM and insomnia patients. Top: Proportion of time spent in all sleep stages (wakefulness, NREM1, NREM2, NREM3 and REM sleep) across the three different groups (GS (controls), INS (insomnia) and SSM patients) for the first half (left) and the second half (right) of the night. Violin plots show individual averages (colored circles) as well as the group median (white circles) and standard deviations (grey bars) across individuals. ANCOVAs were used to assess group-differences for each stage and time period. A Bonferroni correction was applied to correct for multiple comparisons (threshold= 0.05/10). ***: p<0.001; **: p<0.01; *:p<0.05; ns: p>0.5. The horizontal line between the left and right part of the graph denotes the only significant interaction between groups and night-segment. Bottom: Matrices of state-transition probabilities for the three groups (GS, SSM and INS). Each cell shows the probability to transition from a state at time t to another state at time t+1. Probabilities were extracted from the individual hypnograms (30s time-windows). Red-squares show the cells showing a significant modulation across groups according to ANCOVAs. A Bonferroni correction was applied to correct for multiple comparisons (threshold= 0.05/25).

45

1367 1368

Figure 2:

1369 1370 1371

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Figure 2: Spectral features can distinguish good sleepers from insomnia patients with or without SSM. (a) Supervised classifiers were trained on relative power from 0 to 20Hz (step: 0.5Hz) to classify PSG recordings into 3 clinical groups (GS, INS and SSM). The graph shows the classifiers’ performance (ROC curve area), quantifying the discriminability of the different groups at each frequency bin (chance-level: 0.5; see Methods). The procedure was applied for NREM (blue) and REM (green) epochs separately. Colored horizontal bars showed significant clusters when comparing the ROC areas between NREM and REM epochs (cluster permutation; blue: NREM>REM; green: REM>NREM, pcluster<0.001). (b) Relative power spectra for the delta ([0.5-4]Hz), theta/alpha ([5-11]Hz), sigma ([11-15]Hz) and beta ([1620]Hz) bands across the entire night. Power was normalized across the entire [0-20]Hz spectrum and expressed in percentage (see Methods). Each night recording being of a different length, their duration was also normalized and is expressed in percentage. Only epochs in NREM1, NREM2, NREM3 and REM sleep were here considered. Time 0 corresponds to sleep onset (defined as either the first two consecutive epochs of NREM1 or first epoch of NREM2, NREM3 and REM sleep). Black horizontal lines show significance statistical differences across groups as assessed by ANCOVAs at the sample level and corrected for multiple comparisons with a cluster permutation approach (see Methods). Clusters p-values were also corrected for multiple comparisons with Bonferroni (threshold: 0.05/4).

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Figure 3:

Figure 3: CI patients have reduced spectral slopes in NREM sleep (a) Distribution of the slope of the 1/f background of the EEG spectrum (see Methods for details), computed on the power spectrum averaged by sleep stage (Wake, NREM1, NREM2, NREM3 and REM) and across individuals for each group. Violin plots show individual averages (colored circles) as well as the group median (white circles) and standard deviations (grey bars) across individuals. (b) Same data but ordered by ascending slope values in the GS group. ANCOVAs were used to assess group-differences for each sleep-stage. A Bonferroni correction was applied to correct for multiple comparisons (threshold= 0.05/5). ***: p<0.001; **: p<0.01; *:p<0.05; ns: p>0.5.

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Figure 4:

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Figure 4: Properties of sleep slow waves across GS individuals, SSM and INS patients. Panels show the differences across groups for several parameters of sleep slow waves (from top to bottom: density of detected slow waves (waves/minute), peak-to-peak amplitude ( V), slow-wave frequency (Hz) and slope ( V/s); see Methods for details). These properties are shown for slow waves detected in NREM2 (left) and NREM3 (right) for central and frontal electrodes. Violin plots show individual averages (colored circles) as well as the group median (white circles) and standard deviations (grey bars) across individuals. ANCOVAs were used to assess group-differences for each sleep-stage and electrode locations. A Bonferroni correction was applied to correct for multiple comparisons (threshold= 0.05/16). ***: p<0.001; **: p<0.01; *:p<0.05; ns: p>0.5.

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Figure 5:

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Figure 5: Properties of sleep spindles GS individuals, SSM and INS patients. Panels show the differences across groups for several parameters of sleep spindles (from top to bottom: density of detected spindles (spindles/minutes), maximum amplitude of the spindles ( V), duration (s) and frequency (Hz); see Methods for details). These properties are shown for spindles detected in NREM2 (left) and NREM3 (right) for central and frontal electrodes. Violin plots show individual averages (colored circles) as well as the group median (white circles) and standard deviations (grey bars) across individuals. ANCOVAs were used to assess group-difference for each sleep-stage and electrode locations. A Bonferroni correction was applied to correct for multiple comparisons (threshold= 0.05/16). ***: p<0.001; **: p<0.01; *:p<0.05; ns: p>0.5.

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Figure 6:

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Figure 6: Supervised classifiers can automatically diagnose insomnia and insomnia subtypes. A series of classifiers were trained on features obtained from the visual inspection of the PSG recordings (visual, V) or computed from the EEG signal (computed, C) or both (V+C). Different classifications were tested (top: good sleepers (GS) vs. insomnia patients (CI: SSM+INS); middle: GS vs. SSM vs. INS (3 classes); bottom: INS vs. SSM). For each classifier type, 100 iterations of the classification were performed. Violin plots (left) show the distribution of the Cohen’s κ values (classifiers’ accuracy) across iterations. Confusion matrices (left) show the average performance of the visual and computed classifiers (horizontal lines show predicted labels, vertical lines real labels). Number in each cell represents the % of individuals within a particular group (e.g. CI) for which a specific prediction has been made (columns add up to 100%).

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