Sleep stage detection using only heart rate

Getting enough quality sleep plays a vital role in protecting our mental health, physical health, and quality of life. Sleep deprivation can make it difficult to concentrate on daily activities, and lower sleep quality is associated with hypertension, hyperglycemia, and hyperlipidemia. The amount of sleep we get is important, but in recent years, quality sleep has also been deemed significant. Polysomnography, which has been the gold standard in assessing sleep quality based on stages, requires that the subject be attached to electrodes, which can disrupt sleep. An easier method to objectively measure sleep is therefore needed. The aim of this study was to construct an easy and objective sleep stage monitoring method. A cross-sectional study for healthy subjects has been done in our research. A new easy model for monitoring the sleep stages is built on only heart rate calculated by the electrocardiogram. This enabled us to easily assess the sleep quality based on five stages. This experiment included a total of 50 subjects. The overall accuracy in determining the five sleep stages was 66.0 percent. Four stages for sleep are identified accurately compared with other conventional methods. Despite there are no five sleep stage separation method using only heart rate, our method achieved the five separation for sleep with a relatively good accuracy. This study represents a great contribution to the field of sleep science. Because sleep stages can be recognized by the heart rate alone, sleep can be noninvasively assessed with any heart rate meter. This method will make it easier to determine sleep stages and diagnose sleep disorders.

[1]  W. Obrist,et al.  Human cerebral blood flow during sleep and waking. , 1973, Journal of applied physiology.

[2]  S. Cerutti,et al.  Vegetative Background of Sleep Spectral Analysis of the Heart Rate Variability , 1997, Physiology & Behavior.

[3]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[4]  Sheng-Fu Liang,et al.  Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models , 2012, IEEE Transactions on Instrumentation and Measurement.

[5]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[6]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[7]  F. Abboud,et al.  Sympathetic-nerve activity during sleep in normal subjects. , 1993, The New England journal of medicine.

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  A. Chesson,et al.  The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications , 2007 .

[10]  B. Feige,et al.  Sleep Stage Transition Dynamics Reveal Specific Stage 2 Vulnerability in Insomnia , 2017, Sleep.

[11]  H. Akaike.  Fitting autoregressive models for prediction , 1969 .

[12]  Yan Li,et al.  Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal , 2014, IEEE Journal of Biomedical and Health Informatics.

[13]  M. Dumont,et al.  Interdependency between heart rate variability and sleep EEG: linear/non-linear? , 2004, Clinical Neurophysiology.

[14]  Ito Wasito,et al.  Kernel Dimensionality Reduction on Sleep Stage Classification using ECG Signal , 2011 .

[15]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[16]  T. Saibara,et al.  Obstructive sleep apnea syndrome is associated with some components of metabolic syndrome. , 2007, Chest.

[17]  Sabine Van Huffel,et al.  An Evaluation of Cardiorespiratory and Movement Features With Respect to Sleep-Stage Classification , 2014, IEEE Journal of Biomedical and Health Informatics.

[18]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[19]  B. Feige,et al.  Electrodermal activity patterns in sleep stages and their utility for sleep versus wake classification , 2019, Journal of sleep research.

[20]  S. Nevsimalova,et al.  Spectral analysis of heart rate variability in sleep. , 2005, Physiological Research.

[21]  H. Akaike.  A new look at the statistical model identification , 1974 .

[22]  Guy A Dumont,et al.  Circadian variation of heart rate variability across sleep stages. , 2013, Sleep.

[23]  Men-Tzung Lo,et al.  Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms , 2013, Journal of Neuroscience Methods.

[24]  J. Trinder,et al.  Autonomic activity during human sleep as a function of time and sleep stage , 2001, Journal of sleep research.

[25]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[26]  K. Miki,et al.  Functional role of diverse changes in sympathetic nerve activity in regulating arterial pressure during REM sleep. , 2011, Sleep.

[27]  F. Togo,et al.  Decreased fractal component of human heart rate variability during non-REM sleep. , 2001, American journal of physiology. Heart and circulatory physiology.

[28]  Jaspal Singh,et al.  A method of REM-NREM sleep distinction using ECG signal for unobtrusive personal monitoring , 2016, Comput. Biol. Medicine.

[29]  D. Martinez,et al.  Dimensions of sleepiness and their correlations with sleep-disordered breathing in mild sleep apnea. , 2009, Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia.

[30]  Anna M. Bianchi,et al.  Time-varying Analysis of the Heart Rate Variability during REM and Non REM Sleep Stages , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  Xi Long,et al.  Sleep stage classification with ECG and respiratory effort , 2015, Physiological measurement.

[32]  Masashi Shibata,et al.  Sleep Stage Assessment Using Power Spectral Indices of Heart Rate Variability With a Simple Algorithm , 2013, Biological research for nursing.

[33]  S. Chokroverty,et al.  The visual scoring of sleep in adults. , 2007, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[34]  M. Younes,et al.  Assessment of intervention-related changes in non-rapid-eye-movement sleep depth: importance of sleep depth changes within stage 2. , 2017, Sleep medicine.

[35]  T. Penzel,et al.  Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography , 2016, Front. Physiol..

[36]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[37]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[38]  E. Sforza,et al.  Obstructive sleep apnea and the metabolic syndrome in an elderly healthy population: the SYNAPSE cohort , 2012, Sleep and Breathing.