Unconstrained Sleep Stage Estimation Based on Respiratory Dynamics and Body Movement

OBJECTIVES The aim of this study is to establish a sleep monitoring method that can classify sleep into four stages in an unconstrained manner using a polyvinylidene fluoride (PVDF) sensor for continuous and accurate estimation of sleep stages. METHODS The study participants consisted of 12 normal subjects and 13 obstructive sleep apnea (OSA) patients. The physiological signals of the subjects were unconstrainedly measured using the PVDF sensor during polysomnography. The respiration and body movement signals were extracted from the PVDF data. Rapid eye movement (REM) sleep was estimated based on the average rate and variability of the respiratory signal. Wakefulness was detected based on the body movement signal. Variability of the respiratory rate was chosen as an indicator for slow-wave sleep (SWS) detection. Sleep was divided into four stages (wake, light, SWS, and REM) based on the detection results. RESULTS The performance of the method was assessed by comparing the results with a manual scoring by a sleep physician. In an epoch-by-epoch analysis, the method classified the sleep stages with an average accuracy of 70.9 % and kappa statistics of 0.48. No significant differences were observed in the detection performance between the normal and OSA groups. CONCLUSIONS The developed system and methods can be applied to a home sleep monitoring system.

[1]  Nathan Johnson,et al.  Validation of a polyvinylidene fluoride impedance sensor for respiratory event classification during polysomnography. , 2011, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[2]  J. Carrier,et al.  Wake detection capacity of actigraphy during sleep. , 2007, Sleep.

[3]  Kajiro Watanabe,et al.  Noncontact method for sleep stage estimation , 2004, IEEE Transactions on Biomedical Engineering.

[4]  Shwetak N. Patel,et al.  DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar , 2015, UbiComp.

[5]  Meaghan A. O'Reilly,et al.  A PVDF Receiver for Ultrasound Monitoring of Transcranial Focused Ultrasound Therapy , 2010, IEEE Transactions on Biomedical Engineering.

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

[7]  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.

[8]  Feng Wang,et al.  Development of a PVDF Piezopolymer Sensor for Unconstrained In-Sleep Cardiorespiratory Monitoring , 2003 .

[9]  Steffen Leonhardt,et al.  Improvement of Force-Sensor-Based Heart Rate Estimation Using Multichannel Data Fusion , 2015, IEEE Journal of Biomedical and Health Informatics.

[10]  J. Lekkala,et al.  A new method to measure heart rate with EMFi and PVDF materials , 2009, Journal of medical engineering & technology.

[11]  H Dickhaus,et al.  Detection of sleep apnea episodes from multi-lead ECGs considering different physiological influences. , 2007, Methods of information in medicine.

[12]  Conor Heneghan,et al.  Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea , 2006, IEEE Transactions on Biomedical Engineering.

[13]  Atul Malhotra,et al.  Sleep staging based on autonomic signals: a multi-center validation study. , 2011, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[14]  Matteo Matteucci,et al.  Sleep Staging Based on Signals Acquired Through Bed Sensor , 2010, IEEE Transactions on Information Technology in Biomedicine.

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

[16]  Luciane L. de Souza,et al.  Further validation of actigraphy for sleep studies. , 2003, Sleep.

[17]  Ming-Chun Huang,et al.  Unobtrusive Sleep Stage Identification Using a Pressure-Sensitive Bed Sheet , 2014, IEEE Sensors Journal.

[18]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[19]  D. Cvetkovic,et al.  Automatic Sleep Stage Detection and Classification: Distinguishing Between Patients with Periodic Limb Movements, Sleep Apnea Hypopnea Syndrome, and Healthy Controls Using Electrooculography (EOG) Signals , 2015 .

[20]  M. Kryger,et al.  Principles and Practice of Sleep Medicine , 1989 .

[21]  A Burgun,et al.  Automated Classification of Free-text Pathology Reports for Registration of Incident Cases of Cancer , 2011, Methods of Information in Medicine.

[22]  Richard B Berry,et al.  Comparison of respiratory event detection by a polyvinylidene fluoride film airflow sensor and a pneumotachograph in sleep apnea patients. , 2005, Chest.

[23]  David G. Stork,et al.  Pattern Classification , 1973 .

[24]  D. White,et al.  Respiration during sleep in normal man. , 1982, Thorax.

[25]  Meng Xiao,et al.  Sleep stages classification based on heart rate variability and random forest , 2013, Biomed. Signal Process. Control..

[26]  Phillipson Ea,et al.  Control of breathing during sleep. , 1978 .

[27]  Zahra Moussavi,et al.  Sleep Stage Detection Using Tracheal Breathing Sounds: A Pilot Study , 2015, Annals of Biomedical Engineering.

[28]  Ko Keun Kim,et al.  A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals , 2012, IEEE Transactions on Information Technology in Biomedicine.

[29]  D. Jeong,et al.  REM sleep estimation only using respiratory dynamics , 2009, Physiological measurement.

[30]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[31]  J. Wheatley,et al.  Validation of the Sonomat: a contactless monitoring system used for the diagnosis of sleep disordered breathing. , 2014, Sleep.

[32]  A. Viera,et al.  Understanding interobserver agreement: the kappa statistic. , 2005, Family medicine.

[33]  Hau-Tieng Wu,et al.  Assess Sleep Stage by Modern Signal Processing Techniques , 2014, IEEE Transactions on Biomedical Engineering.

[34]  Alexander Tataraidze,et al.  Sleep stage classification based on bioradiolocation signals , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).