Detection of nocturnal slow wave sleep based on cardiorespiratory activity in healthy adults

X. Long, P. Fonseca, R.M. Aarts, R. Haakma, J. Rolink, S. Leonhardt

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
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Abstract

Human slow wave sleep (SWS) during bedtime is paramount for energy conservation and memory consolidation. This work aims at automatically detecting SWS from nocturnal sleep using cardiorespiratory signals that can be acquired with unobtrusive sensors in a home-based scenario. From the signals, time-dependent features are extracted for continuous 30-s epochs. To reduce the measuring noise, body motion artifacts, and/or within-subject variability in physiology conveyed by the features and thus enhance the detection performance, we propose to smooth the features over each night using a spline fitting method. In addition, it was found that the changes in cardiorespiratory activity precede the transitions between SWS and the other sleep stages (non-SWS). To this matter, a novel scheme is proposed that performs the SWS detection for each epoch using the feature values prior to that epoch. Experiments were conducted with a large data set of 325 overnight polysomnography (PSG) recordings using a linear discriminant classifier and ten-fold cross validation. Features were selected with a correlation-based method. Results show that the performance in classifying SWS and non-SWS can be significantly improved when smoothing the features and using the preceding feature values of 5-min earlier. We achieved a Cohen's Kappa coefficient of 0.57 (at an accuracy of 88.8%) using only six selected features for 257 recordings with a minimum of 30-min overnight SWS that were considered representative of their habitual sleeping pattern at home. These features included the standard deviation, low-frequency spectral power, and detrended fluctuation of heartbeat intervals as well as the variations of respiratory frequency and upper and lower respiratory envelopes. A marked drop in Kappa to 0.21 was observed for the other nights with SWS time of less than 30 min which were found to more likely occur in elderly. This will be the future challenge in cardiorespiratory-based SWS detection.
Original languageEnglish
Pages (from-to)123 - 133
JournalIEEE Journal of Biomedical and Health Informatics
Volume21
Issue number1
Early online date2016
DOIs
Publication statusPublished - 2017

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Sleep
Polysomnography
Sleep Stages
Physiology
Artifacts
Noise
Splines
Consolidation
Energy conservation
Classifiers
Data storage equipment
Sensors

Cite this

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title = "Detection of nocturnal slow wave sleep based on cardiorespiratory activity in healthy adults",
abstract = "Human slow wave sleep (SWS) during bedtime is paramount for energy conservation and memory consolidation. This work aims at automatically detecting SWS from nocturnal sleep using cardiorespiratory signals that can be acquired with unobtrusive sensors in a home-based scenario. From the signals, time-dependent features are extracted for continuous 30-s epochs. To reduce the measuring noise, body motion artifacts, and/or within-subject variability in physiology conveyed by the features and thus enhance the detection performance, we propose to smooth the features over each night using a spline fitting method. In addition, it was found that the changes in cardiorespiratory activity precede the transitions between SWS and the other sleep stages (non-SWS). To this matter, a novel scheme is proposed that performs the SWS detection for each epoch using the feature values prior to that epoch. Experiments were conducted with a large data set of 325 overnight polysomnography (PSG) recordings using a linear discriminant classifier and ten-fold cross validation. Features were selected with a correlation-based method. Results show that the performance in classifying SWS and non-SWS can be significantly improved when smoothing the features and using the preceding feature values of 5-min earlier. We achieved a Cohen's Kappa coefficient of 0.57 (at an accuracy of 88.8{\%}) using only six selected features for 257 recordings with a minimum of 30-min overnight SWS that were considered representative of their habitual sleeping pattern at home. These features included the standard deviation, low-frequency spectral power, and detrended fluctuation of heartbeat intervals as well as the variations of respiratory frequency and upper and lower respiratory envelopes. A marked drop in Kappa to 0.21 was observed for the other nights with SWS time of less than 30 min which were found to more likely occur in elderly. This will be the future challenge in cardiorespiratory-based SWS detection.",
author = "X. Long and P. Fonseca and R.M. Aarts and R. Haakma and J. Rolink and S. Leonhardt",
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Detection of nocturnal slow wave sleep based on cardiorespiratory activity in healthy adults. / Long, X.; Fonseca, P.; Aarts, R.M.; Haakma, R.; Rolink, J.; Leonhardt, S.

In: IEEE Journal of Biomedical and Health Informatics, Vol. 21, No. 1, 2017, p. 123 - 133 .

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Detection of nocturnal slow wave sleep based on cardiorespiratory activity in healthy adults

AU - Long, X.

AU - Fonseca, P.

AU - Aarts, R.M.

AU - Haakma, R.

AU - Rolink, J.

AU - Leonhardt, S.

PY - 2017

Y1 - 2017

N2 - Human slow wave sleep (SWS) during bedtime is paramount for energy conservation and memory consolidation. This work aims at automatically detecting SWS from nocturnal sleep using cardiorespiratory signals that can be acquired with unobtrusive sensors in a home-based scenario. From the signals, time-dependent features are extracted for continuous 30-s epochs. To reduce the measuring noise, body motion artifacts, and/or within-subject variability in physiology conveyed by the features and thus enhance the detection performance, we propose to smooth the features over each night using a spline fitting method. In addition, it was found that the changes in cardiorespiratory activity precede the transitions between SWS and the other sleep stages (non-SWS). To this matter, a novel scheme is proposed that performs the SWS detection for each epoch using the feature values prior to that epoch. Experiments were conducted with a large data set of 325 overnight polysomnography (PSG) recordings using a linear discriminant classifier and ten-fold cross validation. Features were selected with a correlation-based method. Results show that the performance in classifying SWS and non-SWS can be significantly improved when smoothing the features and using the preceding feature values of 5-min earlier. We achieved a Cohen's Kappa coefficient of 0.57 (at an accuracy of 88.8%) using only six selected features for 257 recordings with a minimum of 30-min overnight SWS that were considered representative of their habitual sleeping pattern at home. These features included the standard deviation, low-frequency spectral power, and detrended fluctuation of heartbeat intervals as well as the variations of respiratory frequency and upper and lower respiratory envelopes. A marked drop in Kappa to 0.21 was observed for the other nights with SWS time of less than 30 min which were found to more likely occur in elderly. This will be the future challenge in cardiorespiratory-based SWS detection.

AB - Human slow wave sleep (SWS) during bedtime is paramount for energy conservation and memory consolidation. This work aims at automatically detecting SWS from nocturnal sleep using cardiorespiratory signals that can be acquired with unobtrusive sensors in a home-based scenario. From the signals, time-dependent features are extracted for continuous 30-s epochs. To reduce the measuring noise, body motion artifacts, and/or within-subject variability in physiology conveyed by the features and thus enhance the detection performance, we propose to smooth the features over each night using a spline fitting method. In addition, it was found that the changes in cardiorespiratory activity precede the transitions between SWS and the other sleep stages (non-SWS). To this matter, a novel scheme is proposed that performs the SWS detection for each epoch using the feature values prior to that epoch. Experiments were conducted with a large data set of 325 overnight polysomnography (PSG) recordings using a linear discriminant classifier and ten-fold cross validation. Features were selected with a correlation-based method. Results show that the performance in classifying SWS and non-SWS can be significantly improved when smoothing the features and using the preceding feature values of 5-min earlier. We achieved a Cohen's Kappa coefficient of 0.57 (at an accuracy of 88.8%) using only six selected features for 257 recordings with a minimum of 30-min overnight SWS that were considered representative of their habitual sleeping pattern at home. These features included the standard deviation, low-frequency spectral power, and detrended fluctuation of heartbeat intervals as well as the variations of respiratory frequency and upper and lower respiratory envelopes. A marked drop in Kappa to 0.21 was observed for the other nights with SWS time of less than 30 min which were found to more likely occur in elderly. This will be the future challenge in cardiorespiratory-based SWS detection.

U2 - 10.1109/JBHI.2015.2487446

DO - 10.1109/JBHI.2015.2487446

M3 - Article

C2 - 26452293

VL - 21

SP - 123

EP - 133

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

SN - 2168-2194

IS - 1

ER -