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Detailed evaluation of sleep apnea using heart rate variability: a machine learning and statistical method using ECG
1Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
Frontiers in Neurology
|September 26, 2025
Summary
Heart rate variability (HRV) analysis effectively detects sleep apnea by identifying autonomic nervous system changes. Machine learning models using HRV features show high accuracy for non-invasive sleep apnea screening.
Area of Science:
- Cardiology
- Sleep Medicine
- Biomedical Engineering
Background:
- Sleep apnea is linked to autonomic dysfunction and cardiovascular risks.
- Traditional sleep apnea diagnosis (polysomnography) is resource-intensive.
- Heart rate variability (HRV) offers a non-invasive method to assess autonomic changes during apneas.
Purpose of the Study:
- To evaluate the capability of single-lead ECG-derived HRV features in distinguishing apnea from non-apnea states.
- To analyze time-domain, frequency-domain, and nonlinear HRV features for apnea detection.
Main Methods:
- Analysis of 18 subjects from the PhysioNet Apnea-ECG database.
- Extraction of R-R intervals and classification into 1-min apnea/non-apnea epochs.
- Utilized Kubios software for HRV feature extraction and one-way ANOVA for statistical analysis.
Main Results:
- Sympathetic markers (VLF, LF/HF) increased, while parasympathetic markers (HF, RMSSD, SampEn) decreased during apnea (p < 0.05).
- Nonlinear HRV features, like SampEn, demonstrated significant discriminatory power (Cohen's d = 2.93).
- XGBoost model achieved an Area Under the Curve (AUC) of 0.98 for precise apnea detection.
Conclusions:
- HRV parameters, particularly nonlinear and frequency-domain indices, effectively indicate autonomic disruption from sleep-disordered breathing.
- Machine learning-enhanced HRV analysis provides a scalable, non-invasive approach for real-time sleep apnea screening.
- This technique is suitable for integration into wearable health technology and digital sleep medicine platforms.
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