- Email: [email protected]
Assessment of Interaction Between Cardio-Respiratory Signals Using Directed Coherence on Healthy Subjects During Postural Change
IRBM 40 (2019) 167–173
Contents lists available at ScienceDirect
IRBM www.elsevier.com/locate/irbm
Original Article
Assessment of Interaction Between Cardio-Respiratory Signals Using Directed Coherence on Healthy Subjects During Postural Change H. Mary M.C. a,∗ , D. Singh a , K.K. Deepak b a b
Department of Instrumentation and Control, Dr. B. R Ambedkar National Institute of Technology Jalandhar, Punjab, 144011, India Department of Physiology, All India Institute of Medical Science, New Delhi, 110029, India
h i g h l i g h t s
g r a p h i c a l
a b s t r a c t
• Importance of deriving Directed Coherence spectrum on cardiorespiratory system.
• Directed coherence spectrum obtains directional interaction among system.
• Effect of RESP to RR increases during supine but decreases during standing.
• Effect of RR to RESP is found significant only during standing.
a r t i c l e
i n f o
Article history: Received 26 May 2018 Received in revised form 20 January 2019 Accepted 2 April 2019 Available online 24 April 2019 Keywords: ECG Respiration Granger causality Directed coherence Postural change
a b s t r a c t Purpose: To detect and quantify the directional interaction changes between cardio-respiratory system during postural change. Method: Traditional frequency domain analysis based on power spectrum and coherence are insufficient to quantify nonlinear structures and complexity of physiological subsystems. Recently, Granger causality is found as preferable method for evaluation of causality i.e., directional interaction. Frequency domain Granger causality based on directed coherence has been used in this study to identify directional interaction between cardiac and respiratory signal during postural change from supine to standing for healthy subjects. Result: ECG and respiration signal are recorded for this study. The beat-to-beat variability series from ECG provides heart rate (RR) and the respiration amplitude corresponds to RESP time series. It was observed that respiration is responsible for the changes in ECG signal during supine position as compared to standing. The outflow of information from RESP to RR increases during supine results in stronger interaction but reduces during standing result in reduction of interaction. Similarly, the effect of RR on RESP is found significant only during standing. Conclusion: The proposed directed coherence approach detects the cardio-respiratory regulation during postural change and provide information about coupling changes during this transition. © 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.
*
Corresponding author. E-mail addresses: [email protected], [email protected] (H. Mary M.C.). https://doi.org/10.1016/j.irbm.2019.04.002 1959-0318/© 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.
168
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
1. Introduction The study of oscillations in physiological system and its interaction have been under continuing investigation. To understand how the cardio-respiratory system dynamically interacts is one of major exigent problem in cardiac physiology. Dynamic interaction of cardio-respiratory system was first observed by Hales [1]. Investigating the interaction help to identify and differentiate various physiological and pathological states. Initial studies of interaction were based on power spectral analysis but it cannot differentiate direct and indirect interaction. To overcome this limitation, non-linear analysis based on notion of Granger Causality in physiological signals [2] was used. Granger causality is a statistical approach to understand the causal interaction from one time series to another in order to provide a linear prediction about the behavior of a time series from its past by incorporating direct and indirect influences [3]. Various time domain Granger causality analysis includes cross correlation and entropy approach [4], [5]. Since cardio-respiratory signals are rich in oscillatory component, frequency domain Granger causality analysis based on directed coherence (DC) spectrum provides better representation of how the information gets exchanged between these time series [6], [7], [8]. Webber et al., (1994) physiological system changes using recurrence plot [9]. Parati et al., (1995) provided a concise and critical description of the spectral methods most commonly used and its characteristics [10]. Krishnamurthy et al., (2004) dynamic interaction between respiration and cerebral autoregulation during head up tilt [11]. Riedl et al., (2010) identified the potential role of respiration in the study [12]. Kabir et al., (2011) quantifies the cardiorespiratory interaction based on joint symbolic dynamics and observed that respiration effect is reduced during standing [13]. Faes et al., (2011) identified the interaction changes among cardiac, vascular and respiratory system during head up tilt and paced breathing [14]. Montano et al., (2012) proposed a multivariate time series analysis to assess the strength of baroreflex as well as of direct and indirect cardiopulmonary coupling [15]. Iatsenko et al., (2013) used cardiorespiratory interactions on the nonlinear model of coupled oscillators based on the multivariate analysis for different age group [16]. Porta et al., exploit time domain Granger causality approach to disentangle the mechanisms involved in short-term cardiovascular control during pharmacological protocol (using atropine, propranolol, and clonidine) by selectively blocking vagal and sympathetic branches of the autonomic nervous system [17]. Zhang et al., (2015) investigated the changes the cardiorespiratory and cardiovascular based on phase synchronization [18]. Mary et al., (2018) identifies blood pressure reduction during deep breathing due to increased effect of respiration on vascular system [19]. Regardless of the advancement of these new measures for interaction, it is not clear how the information is transferred from one signal to another during cardio-respiratory interaction [20], [5]. In addition, how the interaction varies during the control stimulus [21] to the cardio-respiratory system by changing autonomic nervous system activity based on the application of multivariate autoregressive (MVAR) modeling technique is also found rare [22], [15]. To overcome this, we aimed to analyze the changes in cardio-respiratory system during the autonomic stimulation i.e., during postural change. Furthermore, we identified which system become dominant during supine and standing posture. 2. Materials and methods 2.1. Experimental data We studied 30 healthy young subjects (12 females and 18 males of age 26±4 to nullify age-related variations), which were free
Fig. 1. Illustrative example of a recorded ECG and respiration signal. ECG R-peaks are detected using pan Tompkins and is represented as triangular markers. RESP time series is given by the amplitude of the respiratory signal (back circles on respiration signal) taken at ECG-R peak location.
Fig. 2. The raw RR interval and RESP time series is extracted respectively as temporal interval from ECG (R peak to R peak) and amplitudes of the respiratory signal taken at ECG-R peaks during supine (Sample Number from 0 to 300) and standing (Sample Number from 300 to 600) postures. Time period for supine position is comparatively more as compared to standing posture. Heart rate is more during standing therefore for a shorter duration more number of RR interval can be obtained.
from diseases that can affect autonomic function based on physical examination at the time of study. The study adheres to the ethical standards and was approved by the research advisory committee of human experimentation (national and institutional) and with the Helsinki Declaration of 1975, as revised in 2000 (5) and an informed consent also taken from all subjects. The recording was done using Biopac MP100 system under controlled room environment (quiet room, comfortable light and temperature levels) for 15 minutes in supine and standing posture with 20-sec pause between them. The acquired signals represented in Fig. 1 were the ECG and the respiratory flow digitized at 1000 Hz sampling rate ( f s ) with 12-bit precision. A 3 lead ECG was recorded using Biopac SS2L electrodes placed based on Lead II configuration of Einthoven bipolar system and respiration using the respiratory belt (Biopac SS5LB transducer on the chest for recording chest expansion and contraction. During this recording phase the breathing is not get affected, subject breath normally throughout the recording (range between 13-20 cycles/minute, HF band). 2.2. Extraction of beat to beat RR and RESP time series ECG R-peaks are detected using Tompkins’s algorithm [23]. From the detected R-peak of ECG signal, beat-to-beat variability series of heart rate (RR), respiration (RESP) were then measured offline respectively as the temporal interval from ECG and amplitudes of the respiratory signal taken at ECG-R peaks for each subject. The RR and RESP time series are resampled at a frequency of 4 Hz. The obtained time series are then normalized by subtracting its mean dividing by standard deviation. Three hundred simultaneous samples of RR and RESP were considered for analysis and represented in Fig. 2.
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
169
σm A i j ( f ) γi j ( f ) =
2.3. Calculation of directed coherence power
Sij( f )
The frequency domain Granger causality is first proposed by Alkaike [24] and further named as DC analysis [25] and is based on MVAR. Autoregressive (AR) modeling provide paramount importance in the description multivariate time series. DC quantifies interaction by calculating the amount of the cross-spectral density between time series [6] by considering each series as a combination of all the measured series. DC can be applied for both bivariate and multivariate time series and is derived from the factor of ordinary coherence analysis. To identify the causal interaction, consider MVAR model [15], [7] for a set of M simultaneously acquired time-series Y ( N ) = [ y 1 ( N ), y 2 ( N ), . . . , y M ( N )] of sample length N (Eq. (1)).
⎡ ⎢ ⎣
y1 (N )
⎡
⎤
ai j = A (k)
a11
.. ⎥ = ⎢ .. . ⎦ ⎣ . y M (N ) a M1
· · · a1M .. .. . . · · · aM M
⎤ ⎡ ⎥⎢ ⎦⎣
y 1 ( N − k)
.. .
⎤
⎡
⎥ ⎢ ⎦+⎣
y M ( N − k)
U 1 (N )
⎤
⎥ .. ⎦ . U M (N ) (1)
Where k = 1, 2, · · · , p and p is the model order selected based on Akaike AIC criteria explained in [24] to define the maximum lag allowed to identify interaction. A (k) is M × M model coefficient matrix evaluated based on least square estimation technique [26], were the model coefficient elements ai j quantifies the interaction occurring at lag k from y j to y i (were i, j = 1, 2, · · · , M). U ( N ) represents the input process consist of white and uncorrelated noise. The correlation matrix evaluated for time series Y ( N ) at lag k, is represented as R (k) = E [Y ( N )Y T ( N − k)]. The spectral density matrix S ( f ) represents the Fourier transform of R (k), were S ii ( f ) is the auto spectrum and S i j ( f ) as cross spectrum as diagonal and off diagonal elements respectively. Hence the coherence () can be derived from the spectral density matrix [6] and represented in Eq. (2)
i j ( f ) =
Sij( f ) S ii ( f ) S j j ( f )
(2)
Similarly, the spectral representation of MVAR process can be obtained by taking Fourier transform of Eq. (1) which yield Y ( f ) = A ( f )Y ( f ) + U ( f ), where Y ( f ) and U ( f ) are respectively Fourier transforms of Y ( N ) and U ( N ). The derived Fourier transform of model coefficient matrix is given in Eq. (3). The cross spectrum between signals based on MVAR process is represented in Eq. (4). The power spectral density (PSD) derived using AR modeling is given in Eq. (5) [27]. ∞
A( f ) =
A (k)e
− j2π f kt
(3)
k=−∞
Sij( f ) =
M
σm2 A im ( f ) A ∗jm ( f )
(4)
m =1
P S Dm( f ) =
2 σm
|1 + A ( f )|2
(5)
From the coherence () function (Eq. (6)), the direct coherence (γ ) of individual signals at frequency f can be derived and is given in Eq. (7) and direction of interaction is from y i and y j . However, the power at LF and HF band can be calculated as an area under the curve.
i j ( f ) =
∗ M M
σm T im ( f ) σm T jm ( f ) ∗ γim ( f )γ jm (f) = S ( f ) S ( f ) jj ii m =1 m =1
(6)
(7)
The obtained DC(γ ) are usually complex value, so squared mag-
2
nitude of DC (γi j ) value is used to identify the directionality of causal interaction and is represented as direct coherence spectrum. In cardio-respiratory analysis, the DC spectrum is helpful to identify the link between their oscillatory components. DC can measure both direct and indirect causality. Direct causality between two time-series x and y exists if x → y or y → x, whereas indirect causality occurs when two time-series x and y cause a third common time-series z mediated by one of the two time-series (direct causality: x → y, y ↔ z, indirect causality: x → z mediated by y). To obtain a complete interpretation of which system get interacted, spectral analysis is evaluated at different frequency mainly high frequency (HF: 0.15-0.4 Hz), low frequency (LF: 0.04-0.15 Hz) and very low frequency (VLF: 0.0033-0.04 Hz) band to comprehend the different physiological mechanisms responsible for the autonomic regulation of cardio-respiratory system [28], [29], [30]. From previous literature it given as HF represents parasympathetic activity (mainly respiratory activity), LF corresponding to both sympathetic and parasympathetic but strongly influenced by the oscillatory rhythm of the baroreceptor, and VLF reflect the influence of several physiological mechanisms, including the renin–angiotensin system, vasomotor tone, and thermoregulation [31], [32], [33]. The LF, HF and VLF powers determines the amount of information flow among systems to classify the effect of each system on another in frequency domain. Since VLF constitute non harmonic component which do not have coherent properties so VLF is avoided when interpreting the power spectrum of short-term recordings [33]. The apparent power observed below 0.04 Hz (very low frequency) contributes to total power (T P ) distribution. So in order to remove the very low frequencies (V L F ) in power ( P ) calculation, normalizing procedure given by Eq. (8) [34] is applied.
P=
100. P T P − V LF
(8)
2.4. Statistical analysis Using student t-test the p value was computed to assess the interaction measurement during postural change. Significant statistical difference: ∗ p < 0.05 for supine vs. standing postural change. 3. Result Fig. 2 report an example of the raw time series of RESP and RR measured for a representative subject in supine and standing posture. The statistical variation obtained from mean and standard deviation. The mean of RESP time series is at 0.153 mV at supine and 0.38 mV at standing and are statically significant (p = 0.03). The variability of the respiration measured as the standard deviation (SD) of RESP series increases from supine (0.01198 mV) to standing (0.212 mV) (p = 0.003). The mean RR interval decreased progressively during standing, going from 1100 ms at supine to 780 ms during standing (p = 0.001). The SD of RR did not change significantly (p = 0.18) (supine: 58.3 ms; standing: 62.5 ms). The power spectral density calculated based on auto regressive (AR) modeling using model order 12, for RR interval and RESP time series during supine and standing is depicted in Fig. 3. As from the figure the respiratory frequency occurring around 0.25 Hz in supine position but reduces slightly towards lower side of HF band on respiratory power spectrum during standing. But in RR interval time series power spectrum the HF vanishes and a sudden increase
170
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
Table 1 Absolute power obtained cardio-respiratory interaction (RESP to RR interval and RR interval to RESP) for supine and standing posture. Interaction RESP to RR RR to RESP
Low frequency
High frequency
Supine
Standing
Supine
Standing
5.66 ± 1.94 6.2 ± 5.49
36.3 ± 6.21∗ 27.45 ± 2.12∗
176.4 ± 31.2 7.14 ± 3.3
17.39 ± 5.1∗ 4.19 ± 1.63
Values are expressed as mean ± SD, significance level ∗ p < 0.05 (paired test over 30 subjects).
Fig. 3. Spectral representation using autoregressive modeling (model order 12) of RESP and RR time series during supine and standing postures (a) Power spectrum of RESP time series, respiratory power reduces during standing (b) Power spectrum of RR interval time series, power at LF band increase and HF band disappears during standing.
Table 2 Normalized power obtained cardio-respiratory interaction (RESP to RR interval and RR interval to RESP) for supine and standing posture. Interaction RESP to RR RR to RESP
Low frequency
High frequency
Supine
Standing
Supine
Standing
12 ± 4 24 ± 18
60 ± 18∗ 56 ± 12∗
82 ± 18 48 ± 23
30 ± 13∗ 42 ± 18
Values are expressed as mean ± SD, significance level ∗ p < 0.05 (paired test over 30 subjects).
Fig. 4. Spectral representation of RESP and RR interval time series during supine (a and c) and standing (b and d) using directed coherence approach. Each spectrum in the plot is represented as a combination of RESP (yellow), and RR interval (red). The effect of RR is not present in respiration spectrum (a and b) but in RR spectrum (c and d) respiration effect is present. (a) Respiration spectrum during supine posture (b) Respiration spectrum during standing posture (c) RR interval spectrum during supine posture (d) RR interval spectrum during standing posture.
and 4(d)). In healthy subjects for RESP to RR interaction, the HF power during supine posture (176.4 ± 31.2) is significantly high compared to standing posture (17.39 ± 5.1). In standing posture, a noticeable decrease of LF power (supine: 36.3 ± 6.21 and standing: 5.66 ± 1.94) is also observed form RESP to RR interaction. However, for RR to RESP interaction a significant increase is detected only on LF power (supine: 1.2 ± 5.49 and standing: 27.45 ± 2.12). This observed power change occurring in cardio-respiratory system indicate that RESP to RR is prevailing over RR to RESP interaction for HF power and vice-versa for LF power. Fig. 5 summarizes the averaged coherence variation obtained from direct coherence approach for 30 healthy subjects, which exhibit well-interpretable pattern of interaction results in the generation of cardio-respiratory oscillations. The observed coherence for interaction occurring from respiration to RR is 0.7 during supine at a peak frequency of 0.25 Hz but during standing the mean coherence reduced and also the frequency is shifted to LF band (below 0.15 Hz). Similarly, RR to respiration interaction, significant coherence occurs only on standing posture. Therefore, the respiratory system affects cardiac system during both supine and standing posture but cardiac system affects respiratory system only during standing. The observed coherence indicates the strength of respiratory interaction on ECG varies based on postural state. 4. Discussion
Fig. 5. Illustration of squared magnitude of directed coherence during postural change (a) For interaction from RESP to RR, observed coherence is significant during supine at 0.25 Hz but during standing a shift occurs towards LF band (b) For interaction from RR to RESP, significant coherence occurs only on standing at LF band. It indicates during both supine and standing posture RESP affect RR but only during standing RR affect RESP.
in LF band occurs. The DC spectrum (Fig. 4) assist in determining the direction of interaction by calculating the squared magnitude coherence for individual frequencies and is given in Fig. 5. The calculated power based on DC spectrum for cardio-respiratory interaction for LF and HF band is summarized in Table 1 and its normalized power in Table 2. RESP time series (Fig. 4(a) and 4(b)) does not present any meaningful oscillation in LF band, but for RR interval series LF and HF band are relevant for analysis (Fig. 4(c)
The power spectral density from AR modeling observed a frequency shift on respiratory signal towards lower HF band during standing as compared to supine posture. It makes HF to decrease and LF to increase on RR interval signal. However, it is not clear whether the respiration frequency disappeared or it shifted to LF side on RR interval signal. Also, this power spectral density will not provide the direction of interaction between these signals whether RR affects RESP or RESP affects RR. In order to avoid this misinterpretation and to compute directional information exchange between cardio-respiratory signals, direct coherence concept is applied. DC is ascertained as a suitable method to decipher the change occurring in a multivariate time series and the procedure is explained on section 2.3. The power calculated at LF and HF bands can determine the amount of information flow among interacting systems and can effectively classify the effect of postural change. In RESP to RR interaction an increment of LF power and decrement of HF power is observed during the postural change from supine to standing. It clearly indicates that respiratory effect is dominant in HF band. Consequently, it has also been observed that effect of RESP on
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
RR reduces during standing posture, which reflects the dampening of respiratory sinus arrhythmia that results in the withdrawal of parasympathetic activity and which is in agreement with previous frequency-domain based study by Malliani [35]. Respiratory sinus arrhythmia occurs through the direct and indirect interaction of respiration onto cardiovascular system [14]. RR to RESP interaction produces an increase in LF power with no significant change in HF power. Therefore, the power calculated indicates a clear directional interaction of cardio-respiratory system. The result suggests that the significant decrease in HF power on RESP and increase in LF power on RR observed during postural change from supine to standing indicate proper working of cardio-respiratory system for healthy subjects. If HF power of RR does not decrease during the standing, then an instability in cardio-respiratory system is present and also indication of postural diseases like syncope. The calculated squared magnitude coherence from Fig. 5 indicates that RESP is responsible for the changes in RR signal during supine position but during standing the coherence from RESP to RR reduces. This is the main reason why respiratory frequency in the RR spectrum disappears during standing. Therefore, the drawback of power spectral analysis is removed using this DC approach. Consequently, the effect of significant LF/HF power change and coherence change between RESP and RR indicates that respiratory system is dominant during supine posture and cardiac system during standing posture. The observed information flow is from RESP to RR during supine and RR to RESP during standing indicates that respiratory system and cardiac system works synchronously. Therefore, by studying the amount of power transferred and the coherence using DC help to understand how the cardiac and respiratory system changes during autonomic stimulation. The observation obtained on cardio-respiratory system using DC approach is in agreement with the result found by Marwan et al. [36] based on recurrence plot, thus suggesting that direct interaction is present in cardio-respiratory system. The recurrence plot cannot identify direction of interaction and also cannot compare with standard power spectral density. Hence DC methods provide better prediction in detection of interaction. The applied DC spectral analysis can be further extended to study the cardiorespiratory coupling changes in syncope subjects. Also, better understanding of the measured system will be obtained by incorporating other interacted signal like EEG, Blood pressure, galvanic skin response, etc. It can be extended to study the direction of interaction during other autonomic test like deep breathing test, cold pressor test, etc., and also to other control studies were concurrent systems are working. 5. Conclusion It is observed that, the power distribution varies with both interactions and physiological conditions (postural change). The present study emphasizes on how the interaction occurs between cardiac and respiratory signal during postural change from supine to standing using DC approach based on Granger causality frequency domain. In RESP to RR interaction an increment in LF power and decrement in HF power is observed which indicates respiratory effect is dominant in HF band. In RR to RESP interaction also results in LF power increment with no significant change in HF power. Hence, the observed information flow is dominant from RESP to RR during supine and RR to RESP during standing for healthy subjects. We also identified the dominant frequency shifted towards low frequency during standing posture. This result of frequency shift is in agreement with the power spectral density. Therefore, directed coherence provided a complete behavior of system interaction and also an important tool in understanding the changes in directional interaction during autonomic regulation.
171
Human and animal rights The authors declare that the work described has been carried out in accordance with the Declaration of Helsinki of the World Medical Association revised in 2013 for experiments involving humans as well as in accordance with the EU Directive 2010/63/EU for animal experiments. Informed consent and patient details The authors declare that this report does not contain any personal information that could lead to the identification of the patient(s). The authors declare that they obtained a written informed consent from the patients and/or volunteers included in the article. The authors also confirm that the personal details of the patients and/or volunteers have been removed. Disclosure of interest The authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper. Funding This work did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors. Author contributions All authors attest that they meet the current International Committee of Medical Journal Editors (ICMJE) criteria for Authorship. Acknowledgements The authors would like to thank Biomedical Instrumentation Laboratory, Department of Instrumentation and Control, Dr B R Ambedkar National Institute of Technology Jalandhar and to all volunteers who took part in the recording. References [1] Hales S. Statistical essays: concerning haemastaticks; or, an account of some hydraulick and hydrostatical experiments made on the blood and blood-vessels of animals. London: W Innys and R Manby; 1733. [2] Müller A, Kraemer JF, Penzel T, Bonnemeier H, Kurths J, Wessel N. Causality in physiological signals. Physiol Meas 2016;37(5):R46. [3] Granger CW. Investigating causal relations by econometric models and crossspectral methods. Econometrica 1969:424–38. [4] Porta A, Guzzetti S, Montano N, Pagani M, Somers V, Malliani A, et al. Information domain analysis of cardiovascular variability signals: evaluation of regularity, synchronisation and co-ordination. Med Biol Eng Comput 2000;38(2):180–8. [5] Faes L, Nollo G, Porta A. Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series. Comput Biol Med 2012;42(3):290–7. [6] Kay SM. Modern spectral estimation. Pearson Education India; 1988. [7] Faes L, Nollo G. Multivariate frequency domain analysis of causal interactions in physiological time series. INTECH Open Access Publisher; 2011. [8] Faes L, Erla S, Porta A, Nollo G. A framework for assessing frequency domain causality in physiological time series with instantaneous effects. Philos Trans R Soc, Math Phys Eng Sci 2013;371(1997):20110618. [9] Webber Jr CL, Zbilut JP. Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 1994;76(2):965–73.
172
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
[10] Parati G, Saul JP, Di Rienzo M, Mancia G. Spectral analysis of blood pressure and heart rate variability in evaluating cardiovascular regulation: a critical appraisal. Hypertens 1995;25(6):1276–86. [11] Krishnamurthy S, Wang X, Bhakta D, Bruce E, Evans J, Justice T, et al. Dynamic cardiorespiratory interaction during head-up tilt-mediated presyncope. Am J Physiol, Heart Circ Physiol 2004;287(6):H2510–7. [12] Riedl M, Suhrbier A, Stepan H, Kurths J, Wessel N. Short-term couplings of the cardiovascular system in pregnant women suffering from pre-eclampsia. Philos Trans R Soc, Math Phys Eng Sci 2010;368(1918):2237–50. [13] Kabir MM, Saint DA, Nalivaiko E, Abbott D, Voss A, Baumert M. Quantification of cardiorespiratory interactions based on joint symbolic dynamics. Ann Biomed Eng 2011;39(10):2604. [14] Faes L, Nollo G, Porta A. Information domain approach to the investigation of cardio-vascular, cardio-pulmonary, and vasculo-pulmonary causal couplings. In: Engineering approaches to study cardiovascular physiology: modelling, estimation, and signal processing; 2011. p. 99. [15] Porta A, Bassani T, Bari V, Tobaldini E, Takahashi AC, Catai AM, et al. Modelbased assessment of baroreflex and cardiopulmonary couplings during graded head-up tilt. Comput Biol Med 2012;42(3):298–305. [16] Iatsenko D, Bernjak A, Stankovski T, Shiogai Y, Owen-Lynch PJ, Clarkson PB, et al. Evolution of cardiorespiratory interactions with age. Philos Trans R Soc A 2013;371(1997):20110622. [17] Porta A, Castiglioni P, Di Rienzo M, Bassani T, Bari V, Faes L, et al. Cardiovascular control and time domain Granger causality: insights from selective autonomic blockade. Philos Trans R Soc A 2013;371(1997):20120161. [18] Zhang Q, Patwardhan AR, Knapp CF, Evans JM. Cardiovascular and cardiorespiratory phase synchronization in normovolemic and hypovolemic humans. Eur J Appl Physiol 2015;115(2):417–27. [19] Mary MH, Singh D, Deepak K. Impact of respiration on cardiovascular coupling using Granger causality analysis in healthy subjects. Biomed Signal Process Control 2018;43:196–203. [20] Porta A, Bassani T, Bari V, Pinna GD, Maestri R, Guzzetti S. Accounting for respiration is necessary to reliably infer Granger causality from cardiovascular variability series. IEEE Trans Biomed Eng 2012;59(3):832–41. [21] Porta A, Bari V, Marchi A, De Maria B, Takahashi AC, Guzzetti S, et al. Effect of variations of the complexity of the target variable on the assessment of Wiener–Granger causality in cardiovascular control studies. Physiol Meas 2016;37(2):276. [22] Baselli G, Cerutti S, Badilini F, Biancardi L, Porta A, Pagani M, et al. Model for the assessment of heart period and arterial pressure variability interactions and of respiration influences. Med Biol Eng Comput 1994;32(2):143–52. [23] Pan J, Tompkins WJ. A real-time qrs detection algorithm. IEEE Trans Biomed Eng 1985;3:230–6. [24] Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control 1974;19(6):716–23. [25] Saito Y, Harashima H. Tracking of information within multichannel EEG record causal analysis in EEG. In: Yamaguchi N, Fujisawa K, editors. Recent advances in EEG and EMG data processing. Elsevier; 1981. p. 133–46. [26] Frye RE, Wu M-H. Multichannel least-squares linear regression provides a fast, accurate, unbiased and robust estimation of Granger causality for neurophysiological data. Comput Biol Med 2011;41(12):1118–31. [27] Naidu V, Reddy M. Autoregressive (ar) based power spectral analysis of heart rate time series signal (hrts signal). In: TENCON 2003. Conference on convergent technologies for the Asia-Pacific region, vol. 4. IEEE; 2003. p. 1391–4. [28] Lenis G, Kircher M, Lázaro J, Bailón R, Gil E, Doessel O. Separating the effect of respiration on the heart rate variability using Granger’s causality and linear filtering. Biomed Signal Process Control 2017;31:272–87. [29] Ghouali S, Feham M, Ghouali YZ. The Granger causality effect between cardiorespiratory hemodynamic signals. In: Forging connections between computational mathematics and computational geometry. Springer; 2016. p. 275–300. [30] Ghouali S, Feham M, Ghouali YZ. The direction of information between cardiorespiratory hemodynamic signals: test analysis using Granger causality. GSTF J Math Stat Oper Res 2018;2(2). [31] Freeman R, Chapleau MW. Testing the autonomic nervous system. Handb Clin Neurol 2013;115:115–36. [32] Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Tech. rep., Massachusetts Inst. of Tech. Cambridge Harvard-MIT Div. of Health Science and Technology; 1981. [33] Dantas EM, Sant’Anna ML, Andreão RV, Gonçalves CP, Morra EA, Baldo MP, et al. Spectral analysis of heart rate variability with the autoregressive method: what model order to choose? Comput Biol Med 2012;42(2):164–70. [34] Berntson GG, Thomas Bigger J, Eckberg DL, Grossman P, Kaufmann PG, Malik M, et al. Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 1997;34(6):623–48. [35] Malliani A. The pattern of sympathovagal balance explored in the frequency domain. Physiology 1999;14(3):111–7.
[36] Marwan N, Zou Y, Wessel N, Riedl M, Kurths J. Estimating coupling directions in the cardiorespiratory system using recurrence properties. Philos Trans R Soc A 2013;371(1997):20110624.
Helen Mary M.C. was born in Quilon, Kerala, India, in 1988. She received her B.Tech degree in Applied Electronics and Instrumentation Engineering from LBS Institute of Technology for Women, University of Kerala in 2009, M.Tech degree in Control and Instrumentation from National Institute of Technology Jalandhar, India, in 2013 and currently pursuing Ph.D. degree in the area of Biomedical Signal processing from National Institute of Technology Jalandhar, India. Her research interests include Biomedical Signal Processing, Image Processing, Virtual Instrumentation and Soft Computing. D. Singh received his B.E.(Hons.) degree in Electrical Engineering from Punjab Engineering College, Chandigarh, in 1991, and M.E. degree in Control and Guidance from the University of Roorkee in 1993, and the Ph.D. degree in Engineering from the Indian Institute of Technology Roorkee, in 2004. The Ph.D. degree thesis was developed at the Instrumentation and Signal Processing Laboratory of the Electrical Engineering Department under the direction of Prof. Vinod Kumar, IIT Roorkee; Prof. S. C. Saxena, Ex Director, IIT Roorkee and Prof. K. K. Deepak, All India Institute of Medical Sciences, New Delhi, on ‘Analysis and Interpretation of Heart Rate and Blood Pressure Variability.’ After a brief stint at Goodyear India Limited, Faridabad, in September 1994, he joined the Department of Instrumentation and Control Engineering at the Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, and presently serving as Associate Professor of Instrumentation and Control Engineering there, where he is teaching UG/PG courses related to biomedical engineering, signal processing, and instrumentation and control engineering. His professional research interests are in signal processing, in particular applied to biomedical applications. He has guided five PhDs and is guiding four doctoral and several Master’s theses and has over 80 research publications in internationally reputed Journals and Conference proceedings. He holds membership of professional bodies as Member—IEEE; Life Member—ISTE; Life Member—Instrument Society of India; Life Member— Biomedical Engineering Society of India; Fellow—The Institution of Engineers; and Fellow—The Institution of Electronics and Telecommunication Engineers. He has visited Glasgow University (MEDSIP-2006 and ICE-2013); New Jersey Institute of Technology, New Jersey (IEEE EMBS 2006); City University London and Brunel University London. He is local coordinator of international project ‘Establishment of ECG Database of Healthy Indian Population’ in collaboration with Professor P.W. Macfarlane, University of Glasgow; Professor Vinod Kumar, IIT Roorkee; Professor S. T. Hamde, SGGS Institute of Engineering and Technology, Nanded. He is also the Member Peer Group, VSAT-Enabled Mobile e-Learning Terminals, a project under National Mission on Education through Information and Communication Technology of Ministry of Human Resource Development, Government of India and Member AHEC, IITR Roorkee Team for Performance Testing and Evaluation of Small Hydro Stations in India. He has conducted four AICTE/MHRD short-term courses for the training of faculty of technical institute. He has been the Organizing Secretary of 1st and 2nd International Conferences on Biomedical Engineering and Assistive Technologies (BEATS 2010 and BEATS 2012) at NIT, Jalandhar. Prof. K.K. Deepak is currently serving as Head of the Department of Physiology at AIIMS, New Delhi, India. He initiated the Autonomic Function Lab in the department in 1989. It was the first lab of its kind in the country. It has been providing patient care services and a platform for research in Clinical Physiology. Till date over 16000 human subjects have been investigated in this lab. He has deep interest in biomedical engineering. He developed a blood pressure simulation model. He was involved in harnessing EEG and EOG signal
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
for the purpose of moving prosthesis. He developed the techniques of EMG biofeedback for patients of hand dystonia. He also set up the technique for recording the gastric motility from surface called electrogastrography (EGG) first time in our country. He has also been interested in designing and developing software for analysis of physiological signals. His team
173
has indigenously developed the software for quantification of autonomic tone. He has more than 90 full length refereed research papers published in indexed journals and written 13 chapters in various books. He has coauthored three books. His work has been abstracted in more than 260 scientific communications.
Contents lists available at ScienceDirect
IRBM www.elsevier.com/locate/irbm
Original Article
Assessment of Interaction Between Cardio-Respiratory Signals Using Directed Coherence on Healthy Subjects During Postural Change H. Mary M.C. a,∗ , D. Singh a , K.K. Deepak b a b
Department of Instrumentation and Control, Dr. B. R Ambedkar National Institute of Technology Jalandhar, Punjab, 144011, India Department of Physiology, All India Institute of Medical Science, New Delhi, 110029, India
h i g h l i g h t s
g r a p h i c a l
a b s t r a c t
• Importance of deriving Directed Coherence spectrum on cardiorespiratory system.
• Directed coherence spectrum obtains directional interaction among system.
• Effect of RESP to RR increases during supine but decreases during standing.
• Effect of RR to RESP is found significant only during standing.
a r t i c l e
i n f o
Article history: Received 26 May 2018 Received in revised form 20 January 2019 Accepted 2 April 2019 Available online 24 April 2019 Keywords: ECG Respiration Granger causality Directed coherence Postural change
a b s t r a c t Purpose: To detect and quantify the directional interaction changes between cardio-respiratory system during postural change. Method: Traditional frequency domain analysis based on power spectrum and coherence are insufficient to quantify nonlinear structures and complexity of physiological subsystems. Recently, Granger causality is found as preferable method for evaluation of causality i.e., directional interaction. Frequency domain Granger causality based on directed coherence has been used in this study to identify directional interaction between cardiac and respiratory signal during postural change from supine to standing for healthy subjects. Result: ECG and respiration signal are recorded for this study. The beat-to-beat variability series from ECG provides heart rate (RR) and the respiration amplitude corresponds to RESP time series. It was observed that respiration is responsible for the changes in ECG signal during supine position as compared to standing. The outflow of information from RESP to RR increases during supine results in stronger interaction but reduces during standing result in reduction of interaction. Similarly, the effect of RR on RESP is found significant only during standing. Conclusion: The proposed directed coherence approach detects the cardio-respiratory regulation during postural change and provide information about coupling changes during this transition. © 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.
*
Corresponding author. E-mail addresses: [email protected], [email protected] (H. Mary M.C.). https://doi.org/10.1016/j.irbm.2019.04.002 1959-0318/© 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.
168
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
1. Introduction The study of oscillations in physiological system and its interaction have been under continuing investigation. To understand how the cardio-respiratory system dynamically interacts is one of major exigent problem in cardiac physiology. Dynamic interaction of cardio-respiratory system was first observed by Hales [1]. Investigating the interaction help to identify and differentiate various physiological and pathological states. Initial studies of interaction were based on power spectral analysis but it cannot differentiate direct and indirect interaction. To overcome this limitation, non-linear analysis based on notion of Granger Causality in physiological signals [2] was used. Granger causality is a statistical approach to understand the causal interaction from one time series to another in order to provide a linear prediction about the behavior of a time series from its past by incorporating direct and indirect influences [3]. Various time domain Granger causality analysis includes cross correlation and entropy approach [4], [5]. Since cardio-respiratory signals are rich in oscillatory component, frequency domain Granger causality analysis based on directed coherence (DC) spectrum provides better representation of how the information gets exchanged between these time series [6], [7], [8]. Webber et al., (1994) physiological system changes using recurrence plot [9]. Parati et al., (1995) provided a concise and critical description of the spectral methods most commonly used and its characteristics [10]. Krishnamurthy et al., (2004) dynamic interaction between respiration and cerebral autoregulation during head up tilt [11]. Riedl et al., (2010) identified the potential role of respiration in the study [12]. Kabir et al., (2011) quantifies the cardiorespiratory interaction based on joint symbolic dynamics and observed that respiration effect is reduced during standing [13]. Faes et al., (2011) identified the interaction changes among cardiac, vascular and respiratory system during head up tilt and paced breathing [14]. Montano et al., (2012) proposed a multivariate time series analysis to assess the strength of baroreflex as well as of direct and indirect cardiopulmonary coupling [15]. Iatsenko et al., (2013) used cardiorespiratory interactions on the nonlinear model of coupled oscillators based on the multivariate analysis for different age group [16]. Porta et al., exploit time domain Granger causality approach to disentangle the mechanisms involved in short-term cardiovascular control during pharmacological protocol (using atropine, propranolol, and clonidine) by selectively blocking vagal and sympathetic branches of the autonomic nervous system [17]. Zhang et al., (2015) investigated the changes the cardiorespiratory and cardiovascular based on phase synchronization [18]. Mary et al., (2018) identifies blood pressure reduction during deep breathing due to increased effect of respiration on vascular system [19]. Regardless of the advancement of these new measures for interaction, it is not clear how the information is transferred from one signal to another during cardio-respiratory interaction [20], [5]. In addition, how the interaction varies during the control stimulus [21] to the cardio-respiratory system by changing autonomic nervous system activity based on the application of multivariate autoregressive (MVAR) modeling technique is also found rare [22], [15]. To overcome this, we aimed to analyze the changes in cardio-respiratory system during the autonomic stimulation i.e., during postural change. Furthermore, we identified which system become dominant during supine and standing posture. 2. Materials and methods 2.1. Experimental data We studied 30 healthy young subjects (12 females and 18 males of age 26±4 to nullify age-related variations), which were free
Fig. 1. Illustrative example of a recorded ECG and respiration signal. ECG R-peaks are detected using pan Tompkins and is represented as triangular markers. RESP time series is given by the amplitude of the respiratory signal (back circles on respiration signal) taken at ECG-R peak location.
Fig. 2. The raw RR interval and RESP time series is extracted respectively as temporal interval from ECG (R peak to R peak) and amplitudes of the respiratory signal taken at ECG-R peaks during supine (Sample Number from 0 to 300) and standing (Sample Number from 300 to 600) postures. Time period for supine position is comparatively more as compared to standing posture. Heart rate is more during standing therefore for a shorter duration more number of RR interval can be obtained.
from diseases that can affect autonomic function based on physical examination at the time of study. The study adheres to the ethical standards and was approved by the research advisory committee of human experimentation (national and institutional) and with the Helsinki Declaration of 1975, as revised in 2000 (5) and an informed consent also taken from all subjects. The recording was done using Biopac MP100 system under controlled room environment (quiet room, comfortable light and temperature levels) for 15 minutes in supine and standing posture with 20-sec pause between them. The acquired signals represented in Fig. 1 were the ECG and the respiratory flow digitized at 1000 Hz sampling rate ( f s ) with 12-bit precision. A 3 lead ECG was recorded using Biopac SS2L electrodes placed based on Lead II configuration of Einthoven bipolar system and respiration using the respiratory belt (Biopac SS5LB transducer on the chest for recording chest expansion and contraction. During this recording phase the breathing is not get affected, subject breath normally throughout the recording (range between 13-20 cycles/minute, HF band). 2.2. Extraction of beat to beat RR and RESP time series ECG R-peaks are detected using Tompkins’s algorithm [23]. From the detected R-peak of ECG signal, beat-to-beat variability series of heart rate (RR), respiration (RESP) were then measured offline respectively as the temporal interval from ECG and amplitudes of the respiratory signal taken at ECG-R peaks for each subject. The RR and RESP time series are resampled at a frequency of 4 Hz. The obtained time series are then normalized by subtracting its mean dividing by standard deviation. Three hundred simultaneous samples of RR and RESP were considered for analysis and represented in Fig. 2.
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
169
σm A i j ( f ) γi j ( f ) =
2.3. Calculation of directed coherence power
Sij( f )
The frequency domain Granger causality is first proposed by Alkaike [24] and further named as DC analysis [25] and is based on MVAR. Autoregressive (AR) modeling provide paramount importance in the description multivariate time series. DC quantifies interaction by calculating the amount of the cross-spectral density between time series [6] by considering each series as a combination of all the measured series. DC can be applied for both bivariate and multivariate time series and is derived from the factor of ordinary coherence analysis. To identify the causal interaction, consider MVAR model [15], [7] for a set of M simultaneously acquired time-series Y ( N ) = [ y 1 ( N ), y 2 ( N ), . . . , y M ( N )] of sample length N (Eq. (1)).
⎡ ⎢ ⎣
y1 (N )
⎡
⎤
ai j = A (k)
a11
.. ⎥ = ⎢ .. . ⎦ ⎣ . y M (N ) a M1
· · · a1M .. .. . . · · · aM M
⎤ ⎡ ⎥⎢ ⎦⎣
y 1 ( N − k)
.. .
⎤
⎡
⎥ ⎢ ⎦+⎣
y M ( N − k)
U 1 (N )
⎤
⎥ .. ⎦ . U M (N ) (1)
Where k = 1, 2, · · · , p and p is the model order selected based on Akaike AIC criteria explained in [24] to define the maximum lag allowed to identify interaction. A (k) is M × M model coefficient matrix evaluated based on least square estimation technique [26], were the model coefficient elements ai j quantifies the interaction occurring at lag k from y j to y i (were i, j = 1, 2, · · · , M). U ( N ) represents the input process consist of white and uncorrelated noise. The correlation matrix evaluated for time series Y ( N ) at lag k, is represented as R (k) = E [Y ( N )Y T ( N − k)]. The spectral density matrix S ( f ) represents the Fourier transform of R (k), were S ii ( f ) is the auto spectrum and S i j ( f ) as cross spectrum as diagonal and off diagonal elements respectively. Hence the coherence () can be derived from the spectral density matrix [6] and represented in Eq. (2)
i j ( f ) =
Sij( f ) S ii ( f ) S j j ( f )
(2)
Similarly, the spectral representation of MVAR process can be obtained by taking Fourier transform of Eq. (1) which yield Y ( f ) = A ( f )Y ( f ) + U ( f ), where Y ( f ) and U ( f ) are respectively Fourier transforms of Y ( N ) and U ( N ). The derived Fourier transform of model coefficient matrix is given in Eq. (3). The cross spectrum between signals based on MVAR process is represented in Eq. (4). The power spectral density (PSD) derived using AR modeling is given in Eq. (5) [27]. ∞
A( f ) =
A (k)e
− j2π f kt
(3)
k=−∞
Sij( f ) =
M
σm2 A im ( f ) A ∗jm ( f )
(4)
m =1
P S Dm( f ) =
2 σm
|1 + A ( f )|2
(5)
From the coherence () function (Eq. (6)), the direct coherence (γ ) of individual signals at frequency f can be derived and is given in Eq. (7) and direction of interaction is from y i and y j . However, the power at LF and HF band can be calculated as an area under the curve.
i j ( f ) =
∗ M M
σm T im ( f ) σm T jm ( f ) ∗ γim ( f )γ jm (f) = S ( f ) S ( f ) jj ii m =1 m =1
(6)
(7)
The obtained DC(γ ) are usually complex value, so squared mag-
2
nitude of DC (γi j ) value is used to identify the directionality of causal interaction and is represented as direct coherence spectrum. In cardio-respiratory analysis, the DC spectrum is helpful to identify the link between their oscillatory components. DC can measure both direct and indirect causality. Direct causality between two time-series x and y exists if x → y or y → x, whereas indirect causality occurs when two time-series x and y cause a third common time-series z mediated by one of the two time-series (direct causality: x → y, y ↔ z, indirect causality: x → z mediated by y). To obtain a complete interpretation of which system get interacted, spectral analysis is evaluated at different frequency mainly high frequency (HF: 0.15-0.4 Hz), low frequency (LF: 0.04-0.15 Hz) and very low frequency (VLF: 0.0033-0.04 Hz) band to comprehend the different physiological mechanisms responsible for the autonomic regulation of cardio-respiratory system [28], [29], [30]. From previous literature it given as HF represents parasympathetic activity (mainly respiratory activity), LF corresponding to both sympathetic and parasympathetic but strongly influenced by the oscillatory rhythm of the baroreceptor, and VLF reflect the influence of several physiological mechanisms, including the renin–angiotensin system, vasomotor tone, and thermoregulation [31], [32], [33]. The LF, HF and VLF powers determines the amount of information flow among systems to classify the effect of each system on another in frequency domain. Since VLF constitute non harmonic component which do not have coherent properties so VLF is avoided when interpreting the power spectrum of short-term recordings [33]. The apparent power observed below 0.04 Hz (very low frequency) contributes to total power (T P ) distribution. So in order to remove the very low frequencies (V L F ) in power ( P ) calculation, normalizing procedure given by Eq. (8) [34] is applied.
P=
100. P T P − V LF
(8)
2.4. Statistical analysis Using student t-test the p value was computed to assess the interaction measurement during postural change. Significant statistical difference: ∗ p < 0.05 for supine vs. standing postural change. 3. Result Fig. 2 report an example of the raw time series of RESP and RR measured for a representative subject in supine and standing posture. The statistical variation obtained from mean and standard deviation. The mean of RESP time series is at 0.153 mV at supine and 0.38 mV at standing and are statically significant (p = 0.03). The variability of the respiration measured as the standard deviation (SD) of RESP series increases from supine (0.01198 mV) to standing (0.212 mV) (p = 0.003). The mean RR interval decreased progressively during standing, going from 1100 ms at supine to 780 ms during standing (p = 0.001). The SD of RR did not change significantly (p = 0.18) (supine: 58.3 ms; standing: 62.5 ms). The power spectral density calculated based on auto regressive (AR) modeling using model order 12, for RR interval and RESP time series during supine and standing is depicted in Fig. 3. As from the figure the respiratory frequency occurring around 0.25 Hz in supine position but reduces slightly towards lower side of HF band on respiratory power spectrum during standing. But in RR interval time series power spectrum the HF vanishes and a sudden increase
170
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
Table 1 Absolute power obtained cardio-respiratory interaction (RESP to RR interval and RR interval to RESP) for supine and standing posture. Interaction RESP to RR RR to RESP
Low frequency
High frequency
Supine
Standing
Supine
Standing
5.66 ± 1.94 6.2 ± 5.49
36.3 ± 6.21∗ 27.45 ± 2.12∗
176.4 ± 31.2 7.14 ± 3.3
17.39 ± 5.1∗ 4.19 ± 1.63
Values are expressed as mean ± SD, significance level ∗ p < 0.05 (paired test over 30 subjects).
Fig. 3. Spectral representation using autoregressive modeling (model order 12) of RESP and RR time series during supine and standing postures (a) Power spectrum of RESP time series, respiratory power reduces during standing (b) Power spectrum of RR interval time series, power at LF band increase and HF band disappears during standing.
Table 2 Normalized power obtained cardio-respiratory interaction (RESP to RR interval and RR interval to RESP) for supine and standing posture. Interaction RESP to RR RR to RESP
Low frequency
High frequency
Supine
Standing
Supine
Standing
12 ± 4 24 ± 18
60 ± 18∗ 56 ± 12∗
82 ± 18 48 ± 23
30 ± 13∗ 42 ± 18
Values are expressed as mean ± SD, significance level ∗ p < 0.05 (paired test over 30 subjects).
Fig. 4. Spectral representation of RESP and RR interval time series during supine (a and c) and standing (b and d) using directed coherence approach. Each spectrum in the plot is represented as a combination of RESP (yellow), and RR interval (red). The effect of RR is not present in respiration spectrum (a and b) but in RR spectrum (c and d) respiration effect is present. (a) Respiration spectrum during supine posture (b) Respiration spectrum during standing posture (c) RR interval spectrum during supine posture (d) RR interval spectrum during standing posture.
and 4(d)). In healthy subjects for RESP to RR interaction, the HF power during supine posture (176.4 ± 31.2) is significantly high compared to standing posture (17.39 ± 5.1). In standing posture, a noticeable decrease of LF power (supine: 36.3 ± 6.21 and standing: 5.66 ± 1.94) is also observed form RESP to RR interaction. However, for RR to RESP interaction a significant increase is detected only on LF power (supine: 1.2 ± 5.49 and standing: 27.45 ± 2.12). This observed power change occurring in cardio-respiratory system indicate that RESP to RR is prevailing over RR to RESP interaction for HF power and vice-versa for LF power. Fig. 5 summarizes the averaged coherence variation obtained from direct coherence approach for 30 healthy subjects, which exhibit well-interpretable pattern of interaction results in the generation of cardio-respiratory oscillations. The observed coherence for interaction occurring from respiration to RR is 0.7 during supine at a peak frequency of 0.25 Hz but during standing the mean coherence reduced and also the frequency is shifted to LF band (below 0.15 Hz). Similarly, RR to respiration interaction, significant coherence occurs only on standing posture. Therefore, the respiratory system affects cardiac system during both supine and standing posture but cardiac system affects respiratory system only during standing. The observed coherence indicates the strength of respiratory interaction on ECG varies based on postural state. 4. Discussion
Fig. 5. Illustration of squared magnitude of directed coherence during postural change (a) For interaction from RESP to RR, observed coherence is significant during supine at 0.25 Hz but during standing a shift occurs towards LF band (b) For interaction from RR to RESP, significant coherence occurs only on standing at LF band. It indicates during both supine and standing posture RESP affect RR but only during standing RR affect RESP.
in LF band occurs. The DC spectrum (Fig. 4) assist in determining the direction of interaction by calculating the squared magnitude coherence for individual frequencies and is given in Fig. 5. The calculated power based on DC spectrum for cardio-respiratory interaction for LF and HF band is summarized in Table 1 and its normalized power in Table 2. RESP time series (Fig. 4(a) and 4(b)) does not present any meaningful oscillation in LF band, but for RR interval series LF and HF band are relevant for analysis (Fig. 4(c)
The power spectral density from AR modeling observed a frequency shift on respiratory signal towards lower HF band during standing as compared to supine posture. It makes HF to decrease and LF to increase on RR interval signal. However, it is not clear whether the respiration frequency disappeared or it shifted to LF side on RR interval signal. Also, this power spectral density will not provide the direction of interaction between these signals whether RR affects RESP or RESP affects RR. In order to avoid this misinterpretation and to compute directional information exchange between cardio-respiratory signals, direct coherence concept is applied. DC is ascertained as a suitable method to decipher the change occurring in a multivariate time series and the procedure is explained on section 2.3. The power calculated at LF and HF bands can determine the amount of information flow among interacting systems and can effectively classify the effect of postural change. In RESP to RR interaction an increment of LF power and decrement of HF power is observed during the postural change from supine to standing. It clearly indicates that respiratory effect is dominant in HF band. Consequently, it has also been observed that effect of RESP on
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
RR reduces during standing posture, which reflects the dampening of respiratory sinus arrhythmia that results in the withdrawal of parasympathetic activity and which is in agreement with previous frequency-domain based study by Malliani [35]. Respiratory sinus arrhythmia occurs through the direct and indirect interaction of respiration onto cardiovascular system [14]. RR to RESP interaction produces an increase in LF power with no significant change in HF power. Therefore, the power calculated indicates a clear directional interaction of cardio-respiratory system. The result suggests that the significant decrease in HF power on RESP and increase in LF power on RR observed during postural change from supine to standing indicate proper working of cardio-respiratory system for healthy subjects. If HF power of RR does not decrease during the standing, then an instability in cardio-respiratory system is present and also indication of postural diseases like syncope. The calculated squared magnitude coherence from Fig. 5 indicates that RESP is responsible for the changes in RR signal during supine position but during standing the coherence from RESP to RR reduces. This is the main reason why respiratory frequency in the RR spectrum disappears during standing. Therefore, the drawback of power spectral analysis is removed using this DC approach. Consequently, the effect of significant LF/HF power change and coherence change between RESP and RR indicates that respiratory system is dominant during supine posture and cardiac system during standing posture. The observed information flow is from RESP to RR during supine and RR to RESP during standing indicates that respiratory system and cardiac system works synchronously. Therefore, by studying the amount of power transferred and the coherence using DC help to understand how the cardiac and respiratory system changes during autonomic stimulation. The observation obtained on cardio-respiratory system using DC approach is in agreement with the result found by Marwan et al. [36] based on recurrence plot, thus suggesting that direct interaction is present in cardio-respiratory system. The recurrence plot cannot identify direction of interaction and also cannot compare with standard power spectral density. Hence DC methods provide better prediction in detection of interaction. The applied DC spectral analysis can be further extended to study the cardiorespiratory coupling changes in syncope subjects. Also, better understanding of the measured system will be obtained by incorporating other interacted signal like EEG, Blood pressure, galvanic skin response, etc. It can be extended to study the direction of interaction during other autonomic test like deep breathing test, cold pressor test, etc., and also to other control studies were concurrent systems are working. 5. Conclusion It is observed that, the power distribution varies with both interactions and physiological conditions (postural change). The present study emphasizes on how the interaction occurs between cardiac and respiratory signal during postural change from supine to standing using DC approach based on Granger causality frequency domain. In RESP to RR interaction an increment in LF power and decrement in HF power is observed which indicates respiratory effect is dominant in HF band. In RR to RESP interaction also results in LF power increment with no significant change in HF power. Hence, the observed information flow is dominant from RESP to RR during supine and RR to RESP during standing for healthy subjects. We also identified the dominant frequency shifted towards low frequency during standing posture. This result of frequency shift is in agreement with the power spectral density. Therefore, directed coherence provided a complete behavior of system interaction and also an important tool in understanding the changes in directional interaction during autonomic regulation.
171
Human and animal rights The authors declare that the work described has been carried out in accordance with the Declaration of Helsinki of the World Medical Association revised in 2013 for experiments involving humans as well as in accordance with the EU Directive 2010/63/EU for animal experiments. Informed consent and patient details The authors declare that this report does not contain any personal information that could lead to the identification of the patient(s). The authors declare that they obtained a written informed consent from the patients and/or volunteers included in the article. The authors also confirm that the personal details of the patients and/or volunteers have been removed. Disclosure of interest The authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper. Funding This work did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors. Author contributions All authors attest that they meet the current International Committee of Medical Journal Editors (ICMJE) criteria for Authorship. Acknowledgements The authors would like to thank Biomedical Instrumentation Laboratory, Department of Instrumentation and Control, Dr B R Ambedkar National Institute of Technology Jalandhar and to all volunteers who took part in the recording. References [1] Hales S. Statistical essays: concerning haemastaticks; or, an account of some hydraulick and hydrostatical experiments made on the blood and blood-vessels of animals. London: W Innys and R Manby; 1733. [2] Müller A, Kraemer JF, Penzel T, Bonnemeier H, Kurths J, Wessel N. Causality in physiological signals. Physiol Meas 2016;37(5):R46. [3] Granger CW. Investigating causal relations by econometric models and crossspectral methods. Econometrica 1969:424–38. [4] Porta A, Guzzetti S, Montano N, Pagani M, Somers V, Malliani A, et al. Information domain analysis of cardiovascular variability signals: evaluation of regularity, synchronisation and co-ordination. Med Biol Eng Comput 2000;38(2):180–8. [5] Faes L, Nollo G, Porta A. Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series. Comput Biol Med 2012;42(3):290–7. [6] Kay SM. Modern spectral estimation. Pearson Education India; 1988. [7] Faes L, Nollo G. Multivariate frequency domain analysis of causal interactions in physiological time series. INTECH Open Access Publisher; 2011. [8] Faes L, Erla S, Porta A, Nollo G. A framework for assessing frequency domain causality in physiological time series with instantaneous effects. Philos Trans R Soc, Math Phys Eng Sci 2013;371(1997):20110618. [9] Webber Jr CL, Zbilut JP. Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 1994;76(2):965–73.
172
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
[10] Parati G, Saul JP, Di Rienzo M, Mancia G. Spectral analysis of blood pressure and heart rate variability in evaluating cardiovascular regulation: a critical appraisal. Hypertens 1995;25(6):1276–86. [11] Krishnamurthy S, Wang X, Bhakta D, Bruce E, Evans J, Justice T, et al. Dynamic cardiorespiratory interaction during head-up tilt-mediated presyncope. Am J Physiol, Heart Circ Physiol 2004;287(6):H2510–7. [12] Riedl M, Suhrbier A, Stepan H, Kurths J, Wessel N. Short-term couplings of the cardiovascular system in pregnant women suffering from pre-eclampsia. Philos Trans R Soc, Math Phys Eng Sci 2010;368(1918):2237–50. [13] Kabir MM, Saint DA, Nalivaiko E, Abbott D, Voss A, Baumert M. Quantification of cardiorespiratory interactions based on joint symbolic dynamics. Ann Biomed Eng 2011;39(10):2604. [14] Faes L, Nollo G, Porta A. Information domain approach to the investigation of cardio-vascular, cardio-pulmonary, and vasculo-pulmonary causal couplings. In: Engineering approaches to study cardiovascular physiology: modelling, estimation, and signal processing; 2011. p. 99. [15] Porta A, Bassani T, Bari V, Tobaldini E, Takahashi AC, Catai AM, et al. Modelbased assessment of baroreflex and cardiopulmonary couplings during graded head-up tilt. Comput Biol Med 2012;42(3):298–305. [16] Iatsenko D, Bernjak A, Stankovski T, Shiogai Y, Owen-Lynch PJ, Clarkson PB, et al. Evolution of cardiorespiratory interactions with age. Philos Trans R Soc A 2013;371(1997):20110622. [17] Porta A, Castiglioni P, Di Rienzo M, Bassani T, Bari V, Faes L, et al. Cardiovascular control and time domain Granger causality: insights from selective autonomic blockade. Philos Trans R Soc A 2013;371(1997):20120161. [18] Zhang Q, Patwardhan AR, Knapp CF, Evans JM. Cardiovascular and cardiorespiratory phase synchronization in normovolemic and hypovolemic humans. Eur J Appl Physiol 2015;115(2):417–27. [19] Mary MH, Singh D, Deepak K. Impact of respiration on cardiovascular coupling using Granger causality analysis in healthy subjects. Biomed Signal Process Control 2018;43:196–203. [20] Porta A, Bassani T, Bari V, Pinna GD, Maestri R, Guzzetti S. Accounting for respiration is necessary to reliably infer Granger causality from cardiovascular variability series. IEEE Trans Biomed Eng 2012;59(3):832–41. [21] Porta A, Bari V, Marchi A, De Maria B, Takahashi AC, Guzzetti S, et al. Effect of variations of the complexity of the target variable on the assessment of Wiener–Granger causality in cardiovascular control studies. Physiol Meas 2016;37(2):276. [22] Baselli G, Cerutti S, Badilini F, Biancardi L, Porta A, Pagani M, et al. Model for the assessment of heart period and arterial pressure variability interactions and of respiration influences. Med Biol Eng Comput 1994;32(2):143–52. [23] Pan J, Tompkins WJ. A real-time qrs detection algorithm. IEEE Trans Biomed Eng 1985;3:230–6. [24] Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control 1974;19(6):716–23. [25] Saito Y, Harashima H. Tracking of information within multichannel EEG record causal analysis in EEG. In: Yamaguchi N, Fujisawa K, editors. Recent advances in EEG and EMG data processing. Elsevier; 1981. p. 133–46. [26] Frye RE, Wu M-H. Multichannel least-squares linear regression provides a fast, accurate, unbiased and robust estimation of Granger causality for neurophysiological data. Comput Biol Med 2011;41(12):1118–31. [27] Naidu V, Reddy M. Autoregressive (ar) based power spectral analysis of heart rate time series signal (hrts signal). In: TENCON 2003. Conference on convergent technologies for the Asia-Pacific region, vol. 4. IEEE; 2003. p. 1391–4. [28] Lenis G, Kircher M, Lázaro J, Bailón R, Gil E, Doessel O. Separating the effect of respiration on the heart rate variability using Granger’s causality and linear filtering. Biomed Signal Process Control 2017;31:272–87. [29] Ghouali S, Feham M, Ghouali YZ. The Granger causality effect between cardiorespiratory hemodynamic signals. In: Forging connections between computational mathematics and computational geometry. Springer; 2016. p. 275–300. [30] Ghouali S, Feham M, Ghouali YZ. The direction of information between cardiorespiratory hemodynamic signals: test analysis using Granger causality. GSTF J Math Stat Oper Res 2018;2(2). [31] Freeman R, Chapleau MW. Testing the autonomic nervous system. Handb Clin Neurol 2013;115:115–36. [32] Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Tech. rep., Massachusetts Inst. of Tech. Cambridge Harvard-MIT Div. of Health Science and Technology; 1981. [33] Dantas EM, Sant’Anna ML, Andreão RV, Gonçalves CP, Morra EA, Baldo MP, et al. Spectral analysis of heart rate variability with the autoregressive method: what model order to choose? Comput Biol Med 2012;42(2):164–70. [34] Berntson GG, Thomas Bigger J, Eckberg DL, Grossman P, Kaufmann PG, Malik M, et al. Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 1997;34(6):623–48. [35] Malliani A. The pattern of sympathovagal balance explored in the frequency domain. Physiology 1999;14(3):111–7.
[36] Marwan N, Zou Y, Wessel N, Riedl M, Kurths J. Estimating coupling directions in the cardiorespiratory system using recurrence properties. Philos Trans R Soc A 2013;371(1997):20110624.
Helen Mary M.C. was born in Quilon, Kerala, India, in 1988. She received her B.Tech degree in Applied Electronics and Instrumentation Engineering from LBS Institute of Technology for Women, University of Kerala in 2009, M.Tech degree in Control and Instrumentation from National Institute of Technology Jalandhar, India, in 2013 and currently pursuing Ph.D. degree in the area of Biomedical Signal processing from National Institute of Technology Jalandhar, India. Her research interests include Biomedical Signal Processing, Image Processing, Virtual Instrumentation and Soft Computing. D. Singh received his B.E.(Hons.) degree in Electrical Engineering from Punjab Engineering College, Chandigarh, in 1991, and M.E. degree in Control and Guidance from the University of Roorkee in 1993, and the Ph.D. degree in Engineering from the Indian Institute of Technology Roorkee, in 2004. The Ph.D. degree thesis was developed at the Instrumentation and Signal Processing Laboratory of the Electrical Engineering Department under the direction of Prof. Vinod Kumar, IIT Roorkee; Prof. S. C. Saxena, Ex Director, IIT Roorkee and Prof. K. K. Deepak, All India Institute of Medical Sciences, New Delhi, on ‘Analysis and Interpretation of Heart Rate and Blood Pressure Variability.’ After a brief stint at Goodyear India Limited, Faridabad, in September 1994, he joined the Department of Instrumentation and Control Engineering at the Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, and presently serving as Associate Professor of Instrumentation and Control Engineering there, where he is teaching UG/PG courses related to biomedical engineering, signal processing, and instrumentation and control engineering. His professional research interests are in signal processing, in particular applied to biomedical applications. He has guided five PhDs and is guiding four doctoral and several Master’s theses and has over 80 research publications in internationally reputed Journals and Conference proceedings. He holds membership of professional bodies as Member—IEEE; Life Member—ISTE; Life Member—Instrument Society of India; Life Member— Biomedical Engineering Society of India; Fellow—The Institution of Engineers; and Fellow—The Institution of Electronics and Telecommunication Engineers. He has visited Glasgow University (MEDSIP-2006 and ICE-2013); New Jersey Institute of Technology, New Jersey (IEEE EMBS 2006); City University London and Brunel University London. He is local coordinator of international project ‘Establishment of ECG Database of Healthy Indian Population’ in collaboration with Professor P.W. Macfarlane, University of Glasgow; Professor Vinod Kumar, IIT Roorkee; Professor S. T. Hamde, SGGS Institute of Engineering and Technology, Nanded. He is also the Member Peer Group, VSAT-Enabled Mobile e-Learning Terminals, a project under National Mission on Education through Information and Communication Technology of Ministry of Human Resource Development, Government of India and Member AHEC, IITR Roorkee Team for Performance Testing and Evaluation of Small Hydro Stations in India. He has conducted four AICTE/MHRD short-term courses for the training of faculty of technical institute. He has been the Organizing Secretary of 1st and 2nd International Conferences on Biomedical Engineering and Assistive Technologies (BEATS 2010 and BEATS 2012) at NIT, Jalandhar. Prof. K.K. Deepak is currently serving as Head of the Department of Physiology at AIIMS, New Delhi, India. He initiated the Autonomic Function Lab in the department in 1989. It was the first lab of its kind in the country. It has been providing patient care services and a platform for research in Clinical Physiology. Till date over 16000 human subjects have been investigated in this lab. He has deep interest in biomedical engineering. He developed a blood pressure simulation model. He was involved in harnessing EEG and EOG signal
H. Mary M.C. et al. / IRBM 40 (2019) 167–173
for the purpose of moving prosthesis. He developed the techniques of EMG biofeedback for patients of hand dystonia. He also set up the technique for recording the gastric motility from surface called electrogastrography (EGG) first time in our country. He has also been interested in designing and developing software for analysis of physiological signals. His team
173
has indigenously developed the software for quantification of autonomic tone. He has more than 90 full length refereed research papers published in indexed journals and written 13 chapters in various books. He has coauthored three books. His work has been abstracted in more than 260 scientific communications.