ECG-based automated detection of sleep apnea using deep neural networks and hidden markov models

ECG-based automated detection of sleep apnea using deep neural networks and hidden markov models. Download PDF Abstract Obstructive sleep apnea (OSA) is a common yet underdiagnosed disorder, and current diagnostic methods such as polysomnography are resource-intensive and inaccessible for many patients. Electrocardiogram (ECG)-based screening offers a more practical alternative, but the nonstationary nature of ECG signals and substantial inter-record and physiological variability limit diagnostic accuracy.

Here we propose a deep learning framework that integrates multiple surface ECG-derived features—R-R intervals (RRI), ECG-derived respiration (EDR), and R-wave amplitude (RAMP)—from the PhysioNet Apnea-ECG database. Using a combined Convolutional Neural Network (CNN)–Transformer–Long Short-Term Memory (LSTM) architecture with a Hidden Markov Model (HMM) as a post-processing module, our method achieves an accuracy of 90.33% and specificity of 94.91% under record-wise five-fold cross-validation, while the record-wise A-Test robustness evaluation across K = 2 to K = 36 yielded an accuracy of 90.68% and a specificity of 94.26%. HMM post-processing increased accuracy by about 3.7–4.0% points, with larger improvements observed in sensitivity.

These results demonstrate the feasibility of a computationally efficient ECG-based framework for OSA screening, while further validation on independent external datasets, prospective clinical cohorts, and real-world home or wearable recordings is required before clinical or home-based deployment. Explore related subjects Discover the latest articles and news in related subjects. Cardiology Computational biology and bioinformatics Diseases Engineering Health care Medical research Acknowledgements The authors appreciate the publicly accessible Apnea-ECG database, which enabled the development and evaluation of the proposed methodology.

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