Classification of obstructive and central sleep apnea using wavelet packet analysis of ECG signals

Abstract

Obstructive sleep apnea (OSA) causes a pause in airflow with continuing breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate characteristics of CSA and OSA using wavelet packet analysis of ECG signal over 5 second period and support vector machines. Six patients were used in the study that contained both CSA and OSA events. Eight level wavelet packet analysis was performed on each 5 sec clip using Daubechies (DB3) mother wavelet. Two features namely the best tree and the entropy of the best wavelet tree were extracted from each clip. One patient was used for testing at a time while all other patients' data was used for training. The accuracy range was between 82% and 92% with best tree as features. Entropy of best tree resulted in improved accuracies ranging between 87% and 94.5%.