Recognizing central and obstructive sleep apnea events from normal breathing events in ECG recordings

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 CSA and OSA events from normal breathing events using wavelet based features of ECG signal over 5 second period. Total 164880 epochs(each of 5-second duration) from normal breathing events, 196 epochs from 116 CSA, 5281 epochs from 2173 OSA and 3073 epochs from 1563 hypopnea events were selected from single lead ECGs (sampling rate=250 Hz). At the first stage of classification, apnea events were classified from normal breathing events and at the second stage, hypopneas were identified from all apnea events and at final stage, CSA and OSA types were recognized at 98.96% accuracy. Results indicate the possibility of recognizing OSA/CSA events based on shorter segments of ECG signals.