Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 8, Issue 6, Pages 624-633Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2013.06.001
Keywords
Sleep stage; Heart rate variability; Random forest; Feature importance
Categories
Funding
- State Key Laboratory of Space Medicine Fundamentals and Application
- China Astronaut Research and Training Center [SMFA12B09]
- Advanced Space Medico-Engineering Research Project of China [SJ201006]
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An alternative technique for sleep stages classification based on heart rate variability (HRV) was presented in this paper. The simple subject specific scheme and a more practical subject independent scheme were designed to classify wake, rapid eye movement (REM) sleep and non-REM (NREM) sleep. 41 HRV features extracted from RR sequence of 45 healthy subjects were trained and tested through random forest (RF) method. Among the features, 25 were newly proposed or applied to sleep study for the first time. For the subject independent classifier, all features were normalized with our developed fractile values based method. Besides, the importance of each feature for sleep staging was also assessed by RF and the appropriate number of features was explored. For the subject specific classifier, a mean accuracy of 88.67% with Cohen's kappa statistic kappa of 0.7393 was achieved. While the accuracy and kappa dropped to 72.58% and 0.4627, respectively when the subject independent classifier was considered. Some new proposed HRV features even performed more effectively than the conventional ones. The proposed method could be used as an alternative or aiding technique for rough and convenient sleep stages classification. (C) 2013 Elsevier Ltd. All rights reserved.
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