Automatic sleep apnoea detection using measures of amplitude and heart rate variability from the electrocardiogram

Abstract

A method for the automatic processing of the electrocardiogram (ECG) for the detection of disordered breathing associated with obstructive sleep apnoea is presented. The method provides a minute-by-minute analysis of night-time single lead ECG recordings. An independently validated database of 35 ECG recordings acquired from normal subjects and subjects with obstructive and mixed sleep apnoea, each of approximately eight hours in duration, was used throughout the study. A wide variety of features based on heart beat intervals and an electrocardiogram derived respiratory signal were considered. Classifiers based on linear and quadratic discriminants were used. Results show that a 90% success rate in correctly identifying one-minute segments containing disordered breathing is achievable.