Robust sleep apnea monitoring using heart rate variability and extended Kalman classification based on single lead ECG

Sleep apnea diagnosis requires analysis of long term polysomnographic signal during one period of night sleep. Limited access to sleep laboratories, various required devices and dedicated assistants made the diagnosis of sleep apnea underestimated and not easily accessible to the general population. In this work, a classification method based on modified Kalman filter which uses heart rate variability (HRV) wavelet signal obtained from a single electrocardiogram (ECG) lead is proposed. A pre-filtering was performed on wavelet transform to improve the correlation of extracted features. Sample entropy was used to enhance the convergence rate and accuracy of classification. The performance of the proposed method was evaluated in terms of accuracy, sensitivity and specificity. The classifier overcomes these methods by 5.3% to 7.2% improvements in accuracy.