NDLI: An Online Sleep Apnea Detection Method Based on Recurrence Quantification Analysis

Content Provider	IEEE Xplore Digital Library
Author	Hoa Dinh Nguyen Wilkins, B.A. Qi Cheng Benjamin, B.A.
Copyright Year	2013
Abstract	This paper introduces an online sleep apnea detection method based on heart rate complexity as measured by recurrence quantification analysis (RQA) statistics of heart rate variability (HRV) data. RQA statistics can capture nonlinear dynamics of a complex cardiorespiratory system during obstructive sleep apnea. In order to obtain a more robust measurement of the nonstationarity of the cardiorespiratory system, we use different fixed amount of neighbor thresholdings for recurrence plot calculation. We integrate a feature selection algorithm based on conditional mutual information to select the most informative RQA features for classification, and hence, to speed up the real-time classification process without degrading the performance of the system. Two types of binary classifiers, i.e., support vector machine and neural network, are used to differentiate apnea from normal sleep. A soft decision fusion rule is developed to combine the results of these classifiers in order to improve the classification performance of the whole system. Experimental results show that our proposed method achieves better classification results compared with the previous recurrence analysis-based approach. We also show that our method is flexible and a strong candidate for a real efficient sleep apnea detection system.
Page Count	9
File Size	922329
Starting Page	1285
Ending Page	1293
File Format	PDF
ISSN	21682194
Volume Number	18
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sleep apnea Feature extraction Heart rate variability Mutual information Electrocardiography Support vector machines Biomedical measurement soft decision fusion Feature selection recurrence quantification analysis (RQA) sleep apnea detection
Content Type	Text
Resource Type	Article
Subject	Health Information Management Health Informatics Electrical and Electronic Engineering Computer Science Applications Biotechnology

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