Why the study?
Does a Subspace Discriminant Ensemble classifier using HRV features from a single lead ECG accurately detect the severity of obstructive sleep apnea?
Does a Subspace Discriminant Ensemble classifier using HRV features from a single lead ECG accurately detect the severity of obstructive sleep apnea?
A machine learning classifier using nine HRV features from a single-lead ECG can accurately distinguish severe from non-severe obstructive sleep apnea, offering a simpler alternative to polysomnography.
May support ECG-based OSA severity screening; leaves open validation in larger prospective cohorts before clinical adoption.
Identifying non-polysomnographic predictors of sleep apnea severity is an on-going challenge in the sleep research. In this study, we detect the severity of obstructive sleep apnea (OSA) condition using features derived from a single lead electrocardiogram (ECG) signal. We have explored 17 time domain, frequency domain, and nonlinear Heart Rate Variability (HRV) features to distinguish subjects in the severe and non-severe OSA groups. An optimal set of nine features including Poincare plot based metrics was found to provide significant discriminative information (p-value < 0.05) for classification. On the test dataset of 16 ECG records, a classification accuracy, sensitivity, and specificity of 87.5%, 100%, and 83.33% respectively, have been achieved using Subspace Discriminant Ensemble classifier. Results demonstrate that severe OSA detection using a single lead ECG signal can be more practical yet simpler compared to existing techniques.
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Rahman et al. (2018) studied this question.
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