Detection of Sleep Apnea from surface ECG based on features extracted by an Autoregressive Model

This study proposes an alternative evaluation of obstructive sleep apnea (OSA) based on ECG signal during sleep time. OSA is a common sleep disorder produced by repetitive occlusions in the upper airways. This respiratory disturbance produces a specific pattern on ECG. Extraction of ECG characteristics, as heart rate variability (HRV) and peak R area, offers alternative measures for a sleep apnea pre-diagnosis. 50 recordings coming from the apnea Physionet database were used in the analysis, this database is part of the 70 recordings used for the Computer in Cardiology challenge celebrated in 2000. A bivariate autoregressive model was used to evaluate beat-by-beat power spectral density of HRV and R peak area. K-nearest neighbor (KNN) supervised learning classifier was employed for categorizing apnea events from normal ones, on a minute-by-minute basis for each recording. Data were split into two sets, training and testing set, each one with 25 recordings. The classification results showed an accuracy higher than 85% in both training and testing. In addition it was possible to separate completely between apnea and normal subjects and almost completely among apnea, normal and borderline subjects.