Title :
Unobtrusive Sleep Stage Identification Using a Pressure-Sensitive Bed Sheet
Author :
Samy, Lauren ; Ming-Chun Huang ; Liu, Jason J. ; Wenyao Xu ; Sarrafzadeh, Majid
Author_Institution :
Dept. of Comput. Sci., Univ. of California, Los Angeles, Los Angeles, CA, USA
Abstract :
Sleep constitutes a big portion of our lives and is a major part of health and well-being. Monitoring the quality of sleep can aid in the medical diagnosis of a variety of sleep and psychiatric disorders and can serve as an indication of several chronic diseases. Sleep stage analysis plays a pivotal role in the evaluation of the quality of sleep and is a proven biometric in diagnosing cardiovascular disease, diabetes, and obesity [32]. We describe an unobtrusive framework for sleep stage identification based on a high-resolution pressure-sensitive e-textile bed sheet. We extract a set of sleep-related biophysical and geometric features from the bed sheet and use a two-phase classification procedure for Wake-Non Rapid Eye Movement-Rapid Eye Movement stage identification. A total of seven all-night polysomnography recordings from healthy subjects were used to validate the proposed bed sheet system and the ability to extract sleep stage information from it. When compared with the gold standard, the described system achieved 70.3% precision and 71.1% recall on average. These results suggest that unobtrusive sleep macrostructure analysis could be a viable option in clinical and home settings in the near future. Compared with existing techniques for sleep stage identification, the described system is unobtrusive, fits seamlessly into the user´s familiar sleep environment, and has additional advantages of comfort, low cost, and simplicity.
Keywords :
biomechanics; biomedical equipment; cardiovascular system; diseases; electrocardiography; electroencephalography; eye; medical disorders; medical signal processing; patient monitoring; pressure sensors; sensitivity; signal classification; sleep; textiles; biometrics; cardiovascular disease diagnosis; chronic diseases; diabetes; high-resolution pressure-sensitive e-textile bed sheet; medical diagnosis; obesity; polysomnography recordings; psychiatric disorders; rapid eye movement stage identification; sleep disorders; sleep stage analysis; sleep stage information extraction; sleep-related biophysical features; sleep-related geometric features; two-phase classification procedure; unobtrusive sleep macrostructure analysis; unobtrusive sleep stage identification; wake-nonrapid eye movement; Electrodes; Fabrics; Feature extraction; Monitoring; Sensors; Sleep; Standards; Sleep staging; bed sheet; e-textile; e-textile sleep staging; polysomnography; pressure sensor array; respiratory rate; sleep quality; unobtrusive;
Journal_Title :
Sensors Journal, IEEE
DOI :
10.1109/JSEN.2013.2293917