An Online Sleep Apnea Detection Method Based on Recurrence Quantification Analysis

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.

Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

“…The reason for using the database is its availability in the public domain and its widespread use in the contemporary literature [7,10,8,12]. A total of 35 subjects with OSA are used.…”

“…Hassan and Haque [6] proposed a computerized OSA detection scheme using spectral features in the dual-tree complex wavelet transform domain. Nguyen et al [7] employed heart rate complexity as measured by recurrence quantification analysis statistics of heart rate variability data to classify OSA events. Chen et al [8] propounded a single lead based scheme that utilizes an OSA severity index and support vector machine for OSA diagnosis.…”

Optimal Classifier for Detection of Obstructive Sleep Apnea Using a Heartbeat Signal

“…LR is widely used in medical applications for the ease with which is analyze the relationship between predictors, and an outcome that is dichotomous responses such as the presence or absence of an apnea event [17].…”

“…Smaller values consider only limited neighborhoods. Generally, the choice of k can only be determined empirically [18].…”

Design of a Fast Learning Classifier for Sleep Apnea Database based on Fuzzy SVM

“…Another four parameters were extracted in the frequency domain, including very low-frequency (VLF), low-frequency (LF), high-frequency (HF) and LF/HF ratio. All selected parameters are well known in sleep studies, and have been used in various studies to detect or classify SA [13][14][15]. All 98,060 segments were combined from extracted parameters in the SA database (Table 2).…”