A decision support system for automated identification of sleep stages from single-channel EEG signals

Accepted Manuscript

A decision support system for automated identification of sleep stages from single-channel EEG signals Ahnaf Rashik Hassan, Abdulhamit Subasi PII: DOI: Reference:

S0950-7051(17)30206-X 10.1016/j.knosys.2017.05.005 KNOSYS 3902

To appear in:

Knowledge-Based Systems

Received date: Revised date: Accepted date:

23 December 2016 5 May 2017 6 May 2017

Please cite this article as: Ahnaf Rashik Hassan, Abdulhamit Subasi, A decision support system for automated identification of sleep stages from single-channel EEG signals, Knowledge-Based Systems (2017), doi: 10.1016/j.knosys.2017.05.005

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Highlights

posed.

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• A single channel EEG based automated sleep scoring method is pro-

• A novel signal processing technique, namely TQWT is employed for sleep staging. • We introduce bagging to classify sleep stages.

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• Efficacy of the method is confirmed by statistical and graphical analyses.

• The performance of the proposed scheme, compared to the existing

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ones is promising.

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A decision support system for automated identification of sleep stages from single-channel EEG signals

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Ahnaf Rashik Hassana,b,∗, Abdulhamit Subasic a

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Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada b Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh. c Computer Science Department, College of Engineering, Effat University, Jeddah 21478, Saudi Arabia.

Abstract

A decision support system for automated detection of sleep stages can alleviate the burden of medical professionals of manually annotating a large

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bulk of data, expedite sleep disorder diagnosis, and benefit research. Moreover, the implementation of a sleep monitoring device that is low-power and

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portable requires a reliable and successful sleep stage detection scheme. This article presents a methodology for computer-aided scoring of sleep stages us-

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ing singe-channel EEG signals. EEG signal segments are first decomposed into sub-bands using tunable-Q wavelet transform (TQWT). Four statisti-

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cal moments are then extracted from the resulting TQWT sub-bands. The proposed scheme exploits bootstrap aggregating (Bagging) for classification.

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Efficacy of the feature generation scheme is evaluated using intuitive, statistical, and Fisher criteria analyses. Furthermore, the efficacy of Bagging is evaluated using out-of-bag error analysis. Optimal choices of Bagging ∗

Corresponding Author: Ahnaf Rashik Hassan, Phone: +1-647-3367757, Email: [email protected]

Preprint submitted to Knowledge-Based Systems

May 8, 2017

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and TQWT parameters are explicated. The proposed methodology for automated sleep scoring is tested on the benchmark Sleep-EDF database and

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DREAMS subjects database. Our methodology achieves 92.43%, 93.69%, 94.36%, 96.55%, and 99.75% accuracy for 2-state to 6-state classification of sleep stages on Sleep-EDF database. Experimental results show that the

algorithmic performance of the automated sleep scoring technique presented herein achieves better performance as compared to the state-of-the-art sleep

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staging algorithms. Besides, the proposed scheme performs equally well for two sleep scoring standards, namely- AASM and R&K. Moreover, the proposed decision support system yields high success rate for identifying sleep

states REM and non-REM 1. It can be anticipated that owing to its use of only one channel of EEG signal, the proposed method will be suitable for

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device implementation, eliminate the onus of medical professionals of anno-

diagnosis.

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tating a large volume of recordings manually, and expedite sleep disorder

Keywords: Sleep, EEG, Classification, Wavelet, Statistical moments.

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1. Introduction

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Sleep scoring is the first step of sleep research and various sleep disorder diagnosis. Traditionally, sleep scoring is performed by expert sleep techni-

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cians based on visual observation of signals. Sleep in humans is composed of cyclic alternations of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep states [1]. NREM phase is yet again classified into four states (S1-S4). Stages S4 and S3 are collectively termed as slow wave sleep owing to the dominance of slow oscillations which emanate from neocortex [2]. 3

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Again, sleep stages S2 and S1 together are termed as shallow sleep [3]. For visual sleep scoring, nocturnal polysomnography (PSG) is scored by expert

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sleep scorers in accordance with Rechtschaffen and Kales’s (R&K) guidelines [4] or a recent set of recommendations by American Academy of Sleep Medicine (AASM) [5]. In R&K guidelines, the recordings are annotated in

any one of the 6 stages- S1-S4, Awake (AWA), and REM. The main alterations of AASM guidelines consist of derivations of EEG, “movement time”

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state abolition, combination of stages S4 and S3 into N3, modification of

some context rules in order to make them simpler, and filter setting guidelines [6] [7].

Sleep state detection by examining the EEG signals visually is problematic for many reasons. Sleep stage assessment by technicians is dependent

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on human resources, which is expensive. The process is also dependent on rater’s experience and expertise [8]. Moreover, the huge bulk of data that

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are inspected per patient make sleep scoring by human experts burdensome and subject to misclassification owing to fatigue [9]. In clinical scenarios,

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some cases may demand urgent diagnosis [10] [11]. Manual sleep scoring, due to its time-consuming process, cannot deal with such situations. This

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failure, in turn, leads to belated sleep disorder diagnosis. Again, given its consuming nature, human expert based sleep stage screening is unsuitable to handle large data-sets. As a result, large-scale population studies in sleep

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research are greatly hindered by this caveat [12] [13]. So, it is evident that there is a great need of an automatic sleep scoring system. Now one might ask- why must the sleep scoring scheme be single-channel based? Developing a wearable, unobtrusive, portable, non-invasive, yet low-power

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device is essential for in-home sleep monitoring. The device must be designed and implemented in such a manner that it will reliably conduct a preliminary

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test of the user at home without the requiring a doctor. The development of such a wearable device has gained the interest of sleep research community. A reliable monitoring device of this kind will be instrumental in early diagno-

sis of sleep disordered breathing, such as- sleep apnea and other sleep related disorders. With a view to ensuring portability, convenience of the user, and

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wearability, the automated sleep stage identification scheme employed by the device must be single-channel based. This will also make sure that the battery has an enhanced life and the size of the device is comfortable and convenient for the user. The development and subsequent implementation of such as sleep monitoring system, thus, solely relies on a single-channel based

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reliable computerized sleep staging scheme.

Various prior studies have attempted to perform sleep stage classifica-

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tion [14]. Diykh et al. [15] utilized time domain statistical features and structural graph similarity for feature extraction and K-means in order to

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classify sleep stages. Kouchaki et al. [16] used tensor based singular spectrum analysis for computerized identification of sleep-EEG signals. Hassan

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et al. [17] put forward a computerized sleep staging scheme employing empirical mode decomposition (EMD) and adaptive boosting. Riaz et al. [18] employed spectral and temporal features extracted in the EMD domain to

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devise their computerized sleep scoring scheme. Owing to the use of EMD, both of the aforementioned schemes suffer from mode-mixing problem. Tsinalis et al. [19] implemented a time-frequency analysis based feature extraction and a stacked sparse autoencoders based classification scheme to design

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Figure 1: A scheme of our computer-aided sleep stage scoring framework.

their system for automatic scoring of sleep-EEG. Peker [20] utilized a combination of dual-tree complex wavelet transform (DT-CWT) and Taguchi

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based complex-valued neural network for computerized sleep staging from one channel of EEG signals.

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In the present study, we present a system for automated scoring of sleep states from single-channel EEG signals. The framework proposed herein is

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demonstrated in Fig. 1. The EEG signal segments belonging to various sleep states are first decomposed into sub-bands using a data-driven signal

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analysis technique, namely- tunable Q-factor wavelet transform (TQWT). Four statistical moments, namely- mean, variance, skewness, and kurtosis are then extracted from each of the sub-bands. The test and the train ma-

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trices are formed afterwards. For feature selection and for determining the statistical significance of the differences of the selected features among all the sleep states, Kruskal-Wallis one-way analysis of variance (Kruskal-Wallis ANOVA) is performed. The features that do not pass Kruskal-Wallis ANOVA

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test are removed and the feature matrices are then fed into the classifier. The classifier used in this work is an eminent ensemble-meta learning based clas-

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sification model, namely- bootstrap aggregating (Bagging). Influences and optimal choices of various bagging and TQWT parameters are inspected.

Our experimental findings evince that the algorithmic performance of our

scheme is promising. Algorithmic performance evaluation against the stateof-the-art studies suggest that the proposed sleep scoring scheme performs

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better than or comparable to existing studies in terms of various evaluation metrics. Furthermore, the proposed scheme gives higher detection performance for REM and S1 stages than many of the existing computer-aided sleep scoring schemes.

The organization of the remainder of the article is described below. Sec-

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tion 2 elucidates the proposed feature extraction framework, investigates its efficacy, describes the statistical analysis that has been performed, and de-

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scribes the classifier used in the proposed method. Section 3 expounds the experimentations that have been conducted, presents their results, and expli-

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cates their significance. Finally, Section 4 makes some concluding remarks, points to some of the future directions of this work, and brings this article

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to conclusion.

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2. Materials and methods In this study, Sleep-EDF database available at Physionet and DREAMS

Subjects database have been utilized to conduct the experiments.

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Figure 2: Examples of EEG signal segments of the six sleep states.

2.1. Experimental data

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2.1.1. Sleep-EDF data-set

Sleep-EDF is an open-source, benchmark, and extensively utilized database

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in sleep scoring literature [21], [22], [23], [24]. Eight Caucasian female and male subjects took part in the study. The subjects’ ages ranged from 21-

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35 years. At the time of the study, none of the subjects was not taking any medication. Sleep-EDF has eight recordings. The EEG data have been grouped into two subsets (termed as sc∗ and st∗ ). The first set of EEG data (sc4112e0, sc4102e0, sc4012e0, sc4002e0) have been recorded in a study in 1989. The study subjects were ambulatory healthy. The second set of 8

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EEG data (st7132j0, st7121j0, st7052j0, st7022j0) have been recorded in a study in 1994. The participants of this study felt slight difficulty in falling

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asleep even though all the subjects where health. Data collection has been performed in 24 hours of the subjects’ daily lives. In this subset, nocturnal EEG data have been collected from the four subjects in a hospital with the

help of a miniature telemetry system [25]. For both of the subsets, data of two EEG channels- Fpz-Cz and Pz-Oz have been collected. The sampling

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frequency of the EEG signals is 100 Hz. Prior studies [21], [22], [23], [26] have demonstrated that EEG data from Pz-Oz channel give better algorithmic performance than Fpz-Cz channel. So, data from Pz-Oz channel have been used herein. As per R&K criteria, EEG recordings of both of the subsets have been annotated by an experienced sleep technician on 30s basis.

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Therefore, the duration of each epoch is 30s or 3000 samples. The epochs have been annotated by sleep technicians as- AWA, REM, S1, S2, S3, S4,

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Movement Time, or ‘Unscored’. Fig. 2 shows some EEG signal segments of all the sleep stages. We also consider five cases of classification for both of

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the databases used in this study as Table 1 suggests. The six-state case is the R&K scoring itself- AWA, S1-S4, REM. The 5-state case combines S4

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and S3 of 6-state as slow wave sleep. The 4-state case combine S2 and S1 of 5-state as Table 1 indicates. The 3-state and 2-state cases consist of- REM, AWA, NREM and Sleep (REM and S1-S4), AWA respectively. Since AASM

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guideline combines S3 and S4 into N3 to consider 5 stages of sleep, case I is not considered for AASM scoring. The epoch distribution of each of the states of sleep of this database is shown in Table 2. The total EEG signal segments from Sleep-EDF used in this study is 15,188.

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Table 1: Cases considered for classification in this study.

Number of Classes

Constituent Sleep States

I

6

AWA, REM, S1, S2, S3, S4

II

5

AWA, REM, S1, S2, SWS(S3-S4)

III

4

AWA, REM, S1-S2, SWS(S3-S4)

IV

3

AWA, REM, NREM (S1-S4)

V

2

AWA, Sleep (REM & NREM)

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Case

Table 2: Description of epochs of various sleep states of sleep-EDF database.

S1

S2

S3

S4

REM

Total

8055

604 3621 672

627

1609

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Epochs

AWA

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2.1.2. DREAMS Subjects database

The second database that has been used in this work is DREAMS Subjects database. 20 healthy subjects (16 females, 4 males, 20-65 years old)

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who were not taking any medication, participated in this study and 20 nocturnal PSG have been collected from the study participants. The study has

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been conducted in the sleep lab of Andr Vsale hospital (Montigny-le-Tilleul, Belgium). Each recording is 7-9 hours in duration. DREAMS Subjects has

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168.81 hours of EEG data. The number of EEG segments or epochs extracted from this database is 20,257. A digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium) has been employed to collect the recordings. DREAMS Subjects database contains one submental EMG chan-

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nel, 3 channels of EEG data (C3-A1 or CZ-A1, O1-A1, and FP1-A1) and 2 channels of EOG data (P18-A1, P8-A1) have been recorded. The data have

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been stored in European data format (EDF). The sampling rate of the signals was 200 Hz. Annotations in accordance with both R&K and AASM criteria have been done by an expert sleep technician of the sleep lab. R&K criteria considers sleep states- AWA, REM, S1-S4, ‘sleep stage movement’

and ‘unknow sleep stage’. The unknown sleep stages and movement times

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have been excluded from this study. Again, as per AASM standards, epochs have been annotated in five states- AWA, N1, N2, N3, REM [27]. To carry out the experiments in this study, 50 percent of the Sleep-EDF database has been chosen at random as training data and the other half of the database as testing data. Epochs of each sleep stage were divided

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equally to ensure that both the training and the testing sets contain epochs of all the sleep stages. The same strategy is adopted for DREAMS Subjects

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database as well. In this manner, we have made sure that for each of the two databases, the entire database is used either for training or testing, yet

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never both simultaneously. This data distribution decreases the chance of overfitting as well. The process to divide the databases is repeated 20 times.

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The mean of the performance metrics of this 20 runs have been presented in this paper.

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2.2. Tunable-Q factor wavelet transform Tunable-Q factor wavelet transform (TQWT) is a newly developed signal

decompositions technique that is data-adaptive and flexible. That is why, it has been proven to be an ideal technique to process oscillatory signals [28]. TQWT ensures flexibility by varying its parameters- Q-factor (Q), no. of 11

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H0j (ω)

LP Scaling αj

(a)

s[n]

H1j (ω)

LP Scaling αj−1

HP Scaling β

dj [n]

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(b)

cj [n]

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s[n]

Figure 3: A scheme depicting how an input signal s[n] is decomposed by TQWT up to jth level. (a) the low-pass sub-band signal cj [n] and (b) the high-pass sub-band signal dj [n].

decomposition levels (J), and over-sampling rate (r). Q controls the wavelet’s

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oscillation. The mathematical formulation of Q is given below.

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where

Q=

fc (j) BW (j)

fc (j) = αj

2−β 4α

1 BW (j) = βαj−1 π 2

(1)

(2)

(3)

and j = 1, 2, ..., J. For decomposition of J levels, TQWT converts an

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input signal s[n] into J + 1 sub-bands. For this conversion, a filer bank with two channels is used iteratively. TQWT applies the filter bank to the lowpass sub-band. Input s[n] is decomposed into c0 [n] and d1 [n] in each of the stages. Here, c0 [n] denotes the low-pass sub-band with sampling frequency

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of αfs . d1 [n] indicates the high-pass sub-bands with sampling frequency of βfs . The scaling factors are denoted by α and β and fs is the sampling rate

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of s[n]. Low-pass filter H0 (ω) with low-pass scaling α is applied to generate c0 [n]. d1 [n] is created by H1 (ω) and high-pass scaling β. To make sure that

TQWT has perfect reconstruction and no redundancy, the values of α and

β must be: 0 < α < 1, 0 < β ≤ 1 and α + β > 1. For the implementation of TQWT, we have used the toolbox provided by Selesnick [28].

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Fig. 3 presents a schematic outline of how a signal s[n] is processed by TQWT up to jth level to produce cj [n] and dj [n]. H0j (ω) is the frequency response of the low-pass sub-bad and H1j (ω) is the frequency response of the high-pass sub-band created after j-levels. Their mathematical formulations are given below.

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 0,

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(J)

H0 (ω) =

 Q   J−1 H0 (ω/αm ), if |ω| ≤ αJ π m=0

(J)

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H1 (ω) =

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where

if α π ≤ |ω| ≤ π

 Q  m  H1 (ω/αJ−1 ) J−2  m=0 H0 (ω/α ),      if(1 − β)αJ−1 π ≤ |ω| ≤ αJ−1 π    0,      for otherω ∈ [−π, π]

ω + (β − 1)π ) α+β−1 απ − ω H1 (ω) = θ( ) α+β−1

H0 (ω) = θ(

(4)

J

(5)

(6) (7)

θ(ω) denotes Daubechies filter’s frequency response whose mathematical 13

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formulation is given below. (8)

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p θ(ω) = 0.5(1 + cos(ω)) 2 − cos(ω), |ω| < π TQWT parameters r and Q are related to α and β as: r=

β , 1−α

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2.3. Advantages of TQWT

(9)

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The use of TQWT in the proposed scheme is motivated by various advantages of TQWT over conventional signal processing techniques such as empirical mode decomposition (EMD) and Fourier transform. • The Q-factor of the wavelet must be low as the wavelet transform at-

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tempts to process less oscillatory signals. On the contrary, the Q-factor of the wavelet must be high as the wavelet transform attempts to pro-

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cess highly oscillatory (e.g. EEG) signals. Nevertheless, except for the continuous wavelet transform, most wavelet transforms cannot tune the Q-factor [28]. TQWT eradicates this limitation by tuning its param-

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eter Q. Therefore, it has been successful in analysis and classification

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of EEG.

• Feature generation schemes that use Fourier transform based features,

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assume that the signal is stationary and linear even though EEG is highly non-stationary and nonlinear. In spite of their data-adaptive nature, EMD and almost all of its variations are affected by modemixing. TQWT, on the contrary, does not have this caveat. Therefore, it is more suitable for EEG data analysis. 14

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• TQWT has been proven to be instrumental in analyzing various biosignals successfully of late [29].

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• The transfer functions of TQWT filters are rational, contributing to its less computational burden and efficiency. This also makes the frequency domain specification easier [28].

• The advantage of TQWT over commonly used time-frequency based

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transforms is that TQWT is highly flexible. In other words, TQWT can alter its Q-factor to characterize the morphology of the signal in

the frequency domain in a better manner. Other time-frequency based transforms can not offer this flexibility.

• Last but not the least, wavelet transform’s perfect reconstruction prop-

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erty is inherited by TQWT.

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The advantages mentioned above establish TQWT as a prominent signal decomposition scheme of analyzing oscillatory EEG signals and motivate its use in our framework. Histograms of TQWT sub-band of the six sleep phases

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in Fig. 4 further corroborate with the appealing characteristics of TQWT. It is to be noted in Fig. 4 that the shapes of histograms of the EEG signals are

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different from each other. For instance, histograms corresponding to S1 and S2 are somewhat skewed to the right as opposed to the histograms of other

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sleep stages. Also, the dispersion of the histograms are different from each other. Another key difference among the histograms in Fig. 4 is that the ranges of amplitudes of EEG signals in different sleep stages are different as well. The histograms shapes are different for the six phases of sleep in Fig. 4, conforming yet again the efficacy of TQWT for analyzing sleep-EEG. 15

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S2 State

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Figure 4: TQWT sub-bands’ histograms of the sleep states. Significant differences of histogram shapes are observed among the six stages of sleep. Q = 2, r = 3, J = 15 in this example.

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2.4. Statistical moment based features

Once we have obtained the TQWT sub-bands, the next step is to extract

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four statistical moments to be used as features from each of the sub-bands as Fig. 1 evinces. The use of statistical moments as features in this study

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is motivated by their successful and widespread use in physiological signal analysis [30]. As these measures signify the asymmetry, peakedness, and dis-

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persion of a data distribution, one can anticipate that statistical features will capture the underlying differences of the TQWT sub-bands corresponding to

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the six classes illustrated in Fig. 4. This hypothesis is validated by both the good discrimination power and classification performance of the features as we shall see in subsequent sections of this article. For a TQWT sub-band  with length ζ, T = τ1 , τ2 , τ3 , .., τN mean (θ), variance (σ 2 ), skewness (ι), and kurtosis (ν) can be formulated as shown in Table 3. 16

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Table 3: Description of the four features used in the proposed scheme.

Mathematical Formulation

Mean (θ)

θ= σ2 =

Skewness (ι)

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1 ζ

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2 τi − θ  3

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Kurtosis (ν)

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Feature Name

2.5. Separability evaluation

The efficacy of statistical moments extracted from TQWT sub-bands is yet again demonstrated herein by separability analysis. Our feature genera-

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tion scheme’s effectiveness is now evaluated using Fisher criteria [31]. Unlike most of the separability metrics, Gaussian assumption of the data in consid-

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eration is not required for Fisher criteria [31]. The two Fisher criteria are

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expressed mathematically as follows. J1 =

tr(Sm ) tr(Sw )

J2 = tr(Sw−1 Sm )

(10) (11)

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where Sw and Sm evince the within-class and the between-class scatter

matrices respectively and tr(S) denotes the trace of S. The larger the values

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of J2 and J1 are, the features in consideration are more separable in feature space. Table 4 compares J1 and J2 values of our method with two approaches presented in [32]. It is evident from Table 4 that the J1 and J2 values of

our scheme are higher. This indicates that statistical moments from TQWT sub-bands give high separability among the six sleep phases. 17

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Table 4: Comparison of J1 and J2 of various schemes on Sleep-EDF.

J1

Power spectrum density features [32] STFT and RVM [32] Statistical features and CEEMDAN [30]

0.69

1.82

0.74

4.57

0.88

56.757

0.68

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Spectral features from sleep-EEG signals [33] Proposed Method

2.6. Feature selection

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Methods

1.3147 125.5887

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With a view to ascertaining if the values of the four statistical features differ significantly in the six sleep stages (AWA, S1-S4, and REM) and if

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the discrimination ability of the four statistical features extracted from the TQWT domain are significant statistically, a Kruskal-Wallis one-way analy-

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sis of variance is carried out. An advantage of Kruskal-Wallis test over other hypothesis testing schemes is that it does not require the underlying data

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distribution to be normal [34]. Kruskal-Wallis ANOVA is performed at a confidence level of 95%. Consequently, a difference is regarded to have sig-

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nificance if p < α(= 0.05) is found. The result of Kruskal-Wallis ANOVA is presented in Table 5. We can see that features computed from some of the sub-bands do not pass the hypothesis test. Before classification using the test and the train matrices, these features are removed. Additionally, we can see from Table 5 that of the features that pass the test, almost all of them

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Table 5: p-Values of θ, σ 2 , ι, and ν across various TQWT sub-bands for case I. Statistically insignificant features are shown in bold.

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ι 0.002586 0.003781 0.000514 0.0157 0.1539 0.002926 0.009327 0.002203 0.00999 0.003088 0.005488 0.1265 0.6956 0.008618 0.001954 0.002598 0.007907 0.008866 0.003938 0.008379 0.0085 0.6936 0.006309 0.009433

ν 1.3926e-277 8.7060e-291 8.3727e-243 7.3642e-158 5.2075e-57 2.3317e-277 3.7052e-173 1.6636e-93 4.9284e-94 1.3895e-124 4.5837e-144 2.1401e-134 2.4296e-110 1.4980e-92 6.2755e-69 1.4292e-64 1.4344e-34 7.4337e-40 2.9448e-46 3.0988e-30 6.3235e-29 6.5229e-36 2.1707e-17 1.9109e-07

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σ2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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θ 0.6762 0.002916 0.000821 0.625 0.000585 0.008186 0.00824 0.005205 0.000957 0.0009891 0.008398 0.004177 0.006163 0.002103 0.00026 0.000953 0.001756 0.007923 0.004003 0.00344 0.9744 0.00396 0.00723 0.000564

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Sub-band 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

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give lower p-values evincing good inter-class variation of the features. Thus, the results of Kruskal-Wallis ANOVA confirm the inferences drawn from Fig.

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4 and from Fisher criteria evaluation that the four statistical moments can characterize the sleep stages successfully and the proposed feature extraction scheme is efficacious.

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Algorithm 1: Bagging Input: N number of training examples S = [(xi , yi )] of γ classes having labels yi ∈ ω, ω = {ω1 , ω2 , ...., ωγ } and i = 1, 2, 3, ....., N . Base

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learner or weak classifier or learner WeakLearn. A fraction φ to form

bootstrapped training data-set. T , an integer that indicates the total iterations.

for t = 1, 2, ...., T do 1. Draw a bootstrapped replica St by taking φ percent of S at

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random.

2. Call WeakLearn with St and get the hypothesis (classifier) νt . νt to the ensemble η. end

Test: For a test instance, x with unknown label:

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1. Evaluate η = {ν1 , ν2 , ..., νT } on x.   1,

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2. vt,j =

 0,

if νt picks class ωj Otherwise

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3. Find the total vote obtained by each class: Vj =

T X

vt,j

t=1

where j = 1, 2, .., γ

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4. Select the class that has obtained the highest number of votes as bagging’s output.

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2.7. Bootstrap aggregating Bootstrap aggregating (Bagging) is an ensemble-meta algorithm first pro-

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posed in [35] which creates and combines multiple classification models to solve a particular classification problem. It is one of the simplest and most

intuitive to implement ensemble learning based classifier, even though it yields strikingly good performance [36]. Bagging is depicted Algorithm 1.

Bagging achieves diversity by employing original training data-set’s boot-

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strapped replicas. First, subsets are taken at random with replacement from the original training data-set. Afterwards, each subset St drawn as such is utilized to train a different weak classifier, generally of the same type. In this

way, a hypothesis νt is obtained each time. This weak classifier or predictor is represented as WeakLearn in Algorithm 1. In this article, we use deci-

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sion trees as our weak classifier or learning algorithm. Individually obtained hypotheses are combined afterwards by means of majority voting. For any

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given unlabeled test instance, the ensemble’s decision is the class chosen by most weak classifiers. The number of iterations T and WeakLearn parame-

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ters (e.g. number of leaves for decision tree) affect Bagging’s performance as Algorithm 1 evinces. It is to be noted that T is equal to the total number of

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decision trees (WeakLearn) since one tree is trained in each iteration. The optimal choice of T is important. If T is too small or too large, it will result

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in poor performance or computational inexpensiveness of the algorithm respectively [33]. We shall expound how T and number of leaves are selected

in the next section.

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OOB classification error vs. number of grown trees for case I to V 0.4 Case I Case II Case III Case IV Case V

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0.3 0.25 0.2 0.15

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Out−of−bag classification error

0.35

0.1 0.05

0

50

100 150 200 Number of decision trees

250

300

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0

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Figure 5: Variation of OOB error with the number of decision trees.

3. Results and discussions

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Here we explicate the details and the outcomes of our experiments and expound some of the significance of the results.

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With a view to finding the optimal decision trees required to obtain the

best algorithmic performance and to examining how the accuracy value varies

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with the no. of trees, we find out the no. of decision trees which gives minimum out-of-bag (OOB) error. This process is repeated every time for cases I to V. The resulting values are used to classify the unlabeled test instances in each case. Fig. 5 depicts the variation of OOB error as we vary no. of trees from 1 to 300 for cases I to V. Fig. 5 manifests that after about 22

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LOOCV error variation with number of decision trees 18 Case I Case II Case III Case IV Case V

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16

12 10 8

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LOOCV error (%)

14

6 4

0

50

100 150 200 Number of decision trees

250

300

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2

Figure 6: Variation of LOOCV error with number of decision trees reveals the prediction

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power of the classifier in all the cases.

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20 decision trees, the OOB error almost becomes constant. However, to make sure that the algorithm yields the best performance, no. of trees for which

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OOB error is minimum is noted every time for cases I to V. Afterwards, when classifying the test examples of each case, these values are used. For cases I to V, the optimal no. of decision trees are- 285, 265, 153, 295, and 188

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respectively.

Fig. 5 also manifests the prediction power of bagging for the proposed

feature extraction scheme. In fact, prior studies have empirically demonstrated that OOB estimation is tantamount to using a test data-set having

23

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Variation of accuracy values (%) with the variation of r for cases I−V. 98

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94 92 90 88

Case V Case IV Case III Case II Case I

86 84

4

6

8

10

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Accuracy in percentage

96

12

14

16

18

20

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Value of r

Figure 7: Accuracy variation with r.

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equal size of a train data-set [35]. As OOB error estimates the true ensemble error unbiasedly, cross-validation or a separate test data-set to assess the

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prediction ability of the classification model is not needed anymore. In classification cases where insufficient data are available, this approach for analyzing

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the predictive performance is really helpful. The low OOB error of Fig. 5 also manifests good predictive capability of bagging. Furthermore, after 20

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trees, OOB error variation becomes trivial, indicating the robustness of our method. To further validate the prediction power of the classification model, we have conducted leave-one-out cross-validation (LOOCV) analysis. Fig. 6 shows that the proposed decision support system yields low LOOCV error for a wide range of number of decision trees for all the five cases of interest. 24

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Variation of accuracy values (%) with the variation of Q for cases I−V. 98 96

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92 90 88 86 84 82 80 78

5

10

15

20

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Accuracy in percentage

94

25

30

35

40

45

50

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Value of Q−factor, Q

Figure 8: Accuracy variation with Q-factor Q.

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Thus, LOOCV further validates the prediction capability of the algorithm. The foregoing findings are later validated and confirmed by bagging’s high

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detection accuracy of classifying test data-set. Prior works on TQWT indicate that r ≥ 3 to circumvent excessive un-

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desirable wavelet ringing [28] and higher detection accuracy is obtained if r is lower [37]. The experimental findings of this study confirm this as well.

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Fig. 7 shows how the variation of the value of TQWT parameter r influences the algorithmic performance of the proposed sleep scoring framework. It is clear from Fig. 7 that the highest detection accuracy is obtained when the values of r are set to- 4, 5, 4, 4, and 5 for cases I to V. So, these values of r are used in this study. Again, for Q, the experiments indicate that the 25

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Variation of accuracy values (%) with the number of decomposition levels, J for cases I−V. 98

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94 92 90 88

Case V Case IV Case III Case II Case I

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Accuracy in percentage

96

86 84 82

5

10

15

20

25

30

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Value of number of decomposition levels, J

Figure 9: Accuracy variation with number of decomposition levels J.

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highest detection accuracy is obtained when Q is set to 2, 2, 2, 3, and 1 respectively for cases I to V. This is confirmed by many experimental obser-

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vations wherein the values of the other two parameters J and r have been varied. Fig. 8 serves as a pictorial demonstration of this fact. The detection

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accuracy vs. J does not demonstrate a decreasing trend like Q and r as Fig. 9 evinces. Here the value of J is varied in the range of 1 to 30 in step of one

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setting the optimal values of r and Q in each of the five cases to find out the values of J for which detection accuracy is maximum. The highest detection accuracy is obtained when the value of J is set to 23, 21, 22, 14, and 15.

The parameter value choices have been confirmed and validated from many experimentations. Thus, it can be concluded that the aforementioned values 26

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Table 6: Confusion matrix for case I.

Predicted Class S3

S1

113

43

0

S2

3

1682

23

S3

0

141

116

S4

0

5

27

AWA

27

4

0

REM

20

84

S4

AWA

REM

Sen

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S2

0

56

90

37.42%

8

58

37

92.93%

53

26

0

34.52%

264

18

0

84.35%

0

3971

26

98.61%

1

38

662

82.34%

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Actual Class

S1

0

Table 7: Confusion matrix for case II.

Predicted Class

S3-S4

AWA

REM

Sen

43

0

53

89

38.74%

S2

5

1641

54

70

41

90.66%

S3-S4

0

104

513

33

0

79.04%

AWA

28

6

0

3971

23

98.61%

REM

24

84

1

41

655

81.47%

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Actual Class

117

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S1

S2

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S1

are the optimal values of TQWT parameters.

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The confusion matrices presented in Tables 6-10 give further insights on

the algorithmic performance of our scheme for cases I to V. It is clear from the matrices that in general, as classes increase, the detection accuracy of each sleep stage decreases. This is also reflected on the overall accuracy

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Table 8: Confusion matrix for case III.

Predicted Class AWA

REM

Sen

S1-S2

1879

60

85

89

88.97%

S3-S4

101

515

34

0

79.35%

AWA

63

0

3945

20

97.96%

REM

157

1

32

615

76.49%

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S3-S4

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Actual Class

S1-S2

Table 9: Confusion matrix for case IV.

Predicted Class

Actual Class

AWA

REM

Sen

2592

88

83

93.88%

65

3940

23

97.84%

163

41

601

74.75%

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REM

AWA

M

S1-S4

S1-S4

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Table 10: Confusion matrix for case V.

S1-S4, REM

AWA

Sen

S1-S4, REM

3467

101

97.25%

AWA

113

3915

97.22%

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Actual Class

Actual Class

values for cases I to V. The proposed sleep scoring framework consistently yields high detection accuracy for all the sleep stages except for sleep stage S3 in case I as Table 6 indicates. One of the main reasons of poor per-

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Table 11: S1 detection accuracy of various methods for case I on Sleep-EDF data-set. The highest accuracy value is highlighted in bold.

S1

Doroshenkov et al. [38]

4.84%

Zhu et al. [21]

15.8%

Liang et al. [22]

18.75%

Liang et al. [39]

30%

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Authors

33.70%

Ronzhina et al. [23]

36.17%

Hsu et al. [41]

36.70%

Proposed Method

37.42%

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Vural et al. [40]

Table 12: S1 detection accuracy of various methods for case II on Sleep-EDF data-set.

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The highest accuracy value is highlighted in bold.

Authors

S1

Zhu et al. [21]

15.8%

Liang et al. [22]

18.75%

Liang et al. [39]

30%

Hsu et al. [41]

36.70%

Proposed Method 38.74%

formance of computerized sleep staging algorithms is the resemblance of S1 state’s EEG signals with those of REM’s. In fact, S1 and REM are almost

29

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Table 13: Comparison of performance of various works on Sleep-EDF. Only the highest accuracy value (%) of each method is reported.

Fuzzy logic based iterative method [26]

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Case ICase IICase IIICase IVCase V -

71.2

74.5

88.3

95.4

-

87.20

-

-

-

-

83.60

-

-

-

-

77.98

-

-

-

76.70

-

81.42

88.97

96.90

Temporal features and hidden Markov model [38] 61.08

-

-

-

-

-

-

-

-

Moment based features and adaptive boosting [42] 80.34 82.03

82.83

-

-

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Methods

Frequency bands’ energy, neural network [41] Multiscale entropy, LDA [22]

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Temporal features and hierarchical decision tree [39] PSD of single-channel EEG and ANN [23]

Hybrid features, Karhunen-Love transform [40]

69.98

85.57 86.53

87.49

89.77

95.05

Graph theory based features, SVM [21]

87.50 88.90

89.30

92.60

97.90

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Statistical features and bagging [33]

92.43 93.69 94.36 96.55 99.75

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Proposed Method

indistinguishable by visual inspection [21]. This is why, most S1 epochs are

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misclassified as REM by existing automatic sleep scoring methods. This is also evident from case I confusion matrix in Table 6 and case II confusion

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matrix in Table 7. An advantage of the algorithm propounded herein is that it classifies S1 state better than many of the prior studies. As we can see from the case I confusion matrix in Table 6, our method correctly classifies 37.42% S1 EEG epochs. The same scenario is seen in Table 7 as our method correctly classifies 38.74% S1 EEG epochs in case II. Moreover, in compar30

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ison with the extant state-of-the-art algorithms’ S1 detection performance, the proposed method’s S1 stage identification accuracy is significantly better

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as Tables 11 and 12 suggest. Again, for sleep stage classification, REM phase is very important. REM stage consists of about 5-20% of an adult human’s

nocturnal sleep. Furthermore, detection of REM is a precondition for diagnosing many sleep ailments including narcolepsy, RBD etc. [43]. During

REM, muscle activity is observed to diagnose REM. It is a marker of many

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neurological disorders such as Parkinsons disease. So, REM detection bears

particular significance in the context of sleep data analysis. It can be seen from Tables 6-9 that the proposed algorithm gives 82.34%, 81.47%, 76.49%, and 74.75% accuracy for REM detection for cases I to IV. In comparison, the methods put forward in [21] and [40] give 76.20% and 79.70% REM de-

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tection accuracy respectively for case I as opposed to our method’s 82.34% REM detection accuracy in case I. So, REM phase detection success of our

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algorithm is comparable to or better than the existing studies. To get further insight on the efficacy of the proposed automated sleep

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scoring scheme, the performance of our method is compared with those of the existing works. For fair comparison, classification results obtained from

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Sleep-EDF are considered. Table 13 lists the accuracy values of various studies that use Sleep-EDF. All the accuracy values reported in this section and in Table 13 are the best detection accuracy for a particular method. It

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is clear from Table 13 that the method proposed in this work outperforms others in all the cases. To evaluate the effectiveness of the scheme propounded herein even fur-

ther, we compute Cohen’s kappa coefficient for each of the five cases consid-

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Table 14: Kappa coefficients of the proposed method against various published methods on Sleep-EDF data-set.

-

0.7968

0.81

II

0.7452

0.8

0.83

III

-

0.8125

0.83

IV

-

0.84

0.87

V

-

0.9

0.96

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I

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Case Liang et al. [22] Hassan et al. [30] Zhu et al. [21] Proposed Method 0.836

0.8543 0.863

0.8933 0.9435

ered in this work. Cohens kappa measures the agreement among annotators for categorial items statistically. It has been considered as a more robust measurement of performance than percent agreement since kappa considers the

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coincidentally occurring agreements. Kappa coefficient ranging from 0.81-1 is regarded as almost perfect agreement, 0.61-0.80 as substantial agreement,

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0.41-0.60 as moderate agreement, 0.21-0.40 as fair agreement, 0-0.20 as slight agreement, and less than zero evinces no agreement. Table 14 evinces that

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the proposed scheme gives almost perfect agreement in all the cases of interest. Also, it gives higher accuracy values in comparison with prior works in

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all the cases except in case V. We now present the detection performance of our scheme on DREAMS

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Subjects. Table 15 lists the kappa coefficient and detection accuracy of our algorithm for AASM and R&K standards of sleep scoring. Even though the detection performance of our scheme is promising for all the cases, for case II in both of the scoring standards, accuracy values of case II are on the lower side. However, it can be observed that the proposed algorithm yields consis32

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Table 15: Algorithmic performance of the proposed method on DREAMS Subjects dataset.

R&K

76.39

74.39

Accuracy (%)

AASM

-

78.95

Cohen’s Kappa

R&K

0.785

0.78

Cohen’s Kappa

AASM

-

0.82

81.87

88.98

99.02

83.78

89.04

98.75

0.83

0.882

0.95

0.865

0.91

0.985

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Accuracy (%)

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Evaluation Metric Scoring Scheme Case I Case II Case III Case IV Case V

tently high detection accuracy in general in each of the five cases. Moreover, Table 15 reveals that automated sleep scoring gets more and more difficult for the algorithm with the increase of classes. This result supports the findings of

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prior studies in Table 13. Furthermore, our scheme’s detection performance is quite similar in AASM and R&K scoring as Table 15 manifests. So, our

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sleep scoring system can be employed equally for AASM and R&K scoring scheme without any algorithmic changes or tuning of parameters, which is a big advantage of the system.

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Even though the suitability of TQWT for sleep data analysis has been demonstrated in [44], the proposed method, due to the use of the simple

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and computationally inexpensive statistical features, is particularly suitable for device implementation. apart from application in the clinical scenarios

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for assisting clinicians, one of the goals of developing an automated decision support system for sleep scoring is to implement the algorithm in a wearable and portable sleep monitoring device for in-home care. This necessitates the sleep scoring algorithm to be computationally as inexpensive as possible. The method proposed in this work not only gives high accuracy values, but it is 33

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computationally less expensive. This is owing to the fact that the proposed scheme involves computation of four simple statistical features, whereas the

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Fourier domain based feature extraction scheme increases the computational complexity and expensiveness of the methodology presented in [44]. Thus,

the method presented in this work is more suitable for device implementation than the one presented in [44].

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4. Conclusions and future directions

In this work, a methodology for computer-aided sleep scoring is presented. Sleep-EEG signal segments are first decomposed using TQWT and four statistical moments are extracted and employed as features. Bootstrap aggregating is employed for classification. Our experimental findings manifest that

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statistical moment based features can capture the differences of various sleep states successfully. The influences of various Bagging and TQWT param-

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eters have been demonstrated. The algorithmic performance of our sleep scoring system as opposed to the existing ones is promising. Further, the

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proposed scheme yields good REM and S1 detection accuracy. Moreover, the proposed scheme equally performs well for R&K and AASM sleep scor-

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ing standards. In the future, we shall focus on hardware implementation of the proposed algorithm. A combination of various ensemble learning based

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classification models for the proposed feature extraction framework can be an interesting avenue of further research. Deep learning can also be employed to further meliorate classification performance in all the cases of interest. Future works can also employ time-frequency based methods [45] [46] such as multi-directional kernel methods [47] [9] and wrapper based feature selection 34

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techniques for computerized sleep scoring. To conclude, it can be said that the proposed scheme is efficient and efficacious and will alleviate the burden

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