Automatic classification of sleep apnea epochs using the electrocardiogram

Abstract

This study investigated the automatic prediction of epochs of sleep apnea from the electrocardiogram. A large independently validated database of 70 single lead ECGs, each of approximately 8 hours in duration, was used throughout the study. Thirty five of these records were used for training and 35 retained for independent testing. After considering a wide variety of features the authors found that features based on the power spectral density estimates of the R-wave maxima and R-R intervals to be the most discriminating. Results show that a classification rate of approximately 89% is achievable