Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings

Authors:
Ahsan H. Khandoker;Jayavardhana Gubbi;Marimuthu Palaniswami
Affiliations:
Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Vic, Australia;Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Vic, Australia;Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Vic, Australia
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Year:
2009

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Abstract

Obstructive sleep apnea or hypopnea causes a pause or reduction in airflow with continuous breathing effort. The aim of this study is to identify individual apnea and hypopnea events from normal breathing events using wavelet-based features of 5-s ECG signals (sampling rate = 250 Hz) and estimate the surrogate apnea index (AI)/hypopnea index (HI) (AHI). Total 82535 ECG epochs (each of 5-s duration) from normal breathing during sleep, 1638 ECG epochs from 689 hypopnea events, and 3151 ECG epochs from 1862 apnea events were collected from 17 patients in the training set. Two-staged feedforward neural network model was trained using features from ECG signals with leave-one-patient-out cross-validation technique. At the first stage of classification, events (apnea and hypopnea) were classified from normal breathing events, and at the second stage, hypopneas were identified from apnea. Independent test was performed on 16 subjects' ECGs containing 483 hypopnea and 1352 apnea events. The cross-validation and independent test accuracies of apnea and hypopnea detection were found to be 94.84% and 76.82%, respectively, for training set, and 94.72% and 79.77%, respectively, for test set. The Bland-Altman plots showed unbiased estimations with standard deviations of ± 2.19, ± 2.16, and ± 3.64 events/h for AI, HI, and AHI, respectively. Results indicate the possibility of recognizing apnea/hypopnea events based on shorter segments of ECG signals.