Obstructive sleep apnea detection based on electrocardiogram analysis

Abstract

Obstructive Sleep Apnea is a frequent disorder with detrimental health, performance and safety effects. The diagnosis of the disorder is cumbersome and expensive. New methods for screening and diagnosis are needed. The method the authors describe in this work is based on detection of respiratory disturbance during sleep from continuously monitored ECG during the night. The detection is based on QRS complex changes caused by apneas and spectral abnormalities in the Heart Rate Variability which are related to recurrent respiratory events. An automatic step-by-step approach, based on these two already described phenomena, was developed, validated and then applied on data supplied by Physionet for the CinC Challenge 2000. The methods yield results in excellent agreement with the manually scored standard sleep studies, and achieved 14788/17262 correct classifications on minute-by-minute bases.