Power spectral analysis for identifying the onset and termination of obstructive sleep apnoea events in ECG recordings

Abstract

The high prevalence of obstructive sleep apnoea (OSA) requires a simplified, unattended screening device that would be useful for diagnosis at the early stage. This study presents a method for screening individual OSA event based on sleep ECG signal. The overnight ECG recordings were divided into 5-second epochs containing normal (N) breathing and onset (O), maximum (M) & termination (T) of OSA events. Power spectral analysis of ECG epochs was employed to extract features. The area under receiver operating characteristics curve was estimated to determine the discrimination capability of each feature (or power in each frequency bin). The maximum ROC areas for N/O, N/M and N/T were found to be..