Abstract
BACKGROUND: Sleep apnea is a common sleep disorder associated with high degree of autonomic dysfunction and increased cardiovascular risk. Traditional diagnostic methods such as polysomnography (PSG) are costly, time-consuming, and sometimes unavailable. Heart rate variability (HRV), a noninvasively assessable measure, is another promising method for the assessment of autonomic perturbations during apneas. The objective of this study was to investigate the extent to which features derived from single-lead ECG are capable of differentiating apnea from non-apnea states in time-domain, frequency-domain and nonlinear HRV features. METHODS: Analysis was done on 18 subjects from the PhysioNet Apnea-ECG database. After preprocessing to extract R-R intervals, the ECG signals were divided into 1-min epochs and classified as either apnea or non-apnea. Kubios software was used to extract HRV features, and one-way ANOVA was used for statistical comparison. RESULTS: The predictability of HRV features was analyzed using machine learning classifiers Random Forest and XGBoost. Sympathetic markers (VLF and LF/HF) increased, while parasympathetic-related features (HF, RMSSD, SampEn) decreased during apnea (p < 0.05). Nonlinear features, including SampEn, showed high discriminatory performance (Cohen's d = 2.93). The AUC of XGBoost model reached to 0.98, demonstrating the usefulness of the HRV features in precise apnea detection. CONCLUSION: HRV parameters can efficiently reflect autonomic disruption induced by SAAs, especially nonlinear and frequency domain indices. Augmented by machine learning, HRV analysis is a powerful and scalable technique toward real-time, non-invasive screening of sleep disordered breathing that can be implemented in to wearable health technology and digital sleep medicine.