Ensemble classification approach for screening of obstructive sleep apnoea using ECG

Abstract

Sleep is essential for healthy human life. Obstructive Sleep Apnoea (OSA) is a commonly occurring sleep breathing disorder and manifests itself as pauses in nocturnal breathing. Screening is traditionally done at sleep labs, but is considered as labour intensive and costly procedure. Hence, reduced complexity methods for screening of OSA are necessary. Heart Rate Variability (HRV) is widely accepted as a characteristic of OSA. This paper proposes an ensemble learning based classifier for the detection of OSA through HRV. Sleep signals were acquired from benchmarked databases such as Apnoea-ECG and UCCD. Several linear and non-linear features that includes time domain, frequency domain and statistical features are utilised. Dimensionality reduction was done for deriving the optimal feature set. Ten-fold cross validation test was performed and a maximum accuracy of 94% was achieved in detecting OSA occurrences. The results of the proposed study are comparable to the gold standard of OSA screening.