Key result
CNN-BiLSTM model classifies obstructive sleep apnea severity from single-lead ECG with ~95% accuracy.
Why the study?
Obstructive sleep apnea remains largely underdiagnosed because standard polysomnography is costly, time-consuming, and unavailable in many healthcare settings.
Does an explainable CNN-BiLSTM framework improve multi-class OSA severity detection accuracy compared to conventional machine learning algorithms using single-lead ECG signals?
Does an explainable CNN-BiLSTM framework improve multi-class OSA severity detection accuracy compared to conventional machine learning algorithms using single-lead ECG signals?
Absolute Event Rate: 94.7% vs 91.8%
p-value: p=<0.001
An explainable CNN-BiLSTM deep learning framework using single-lead ECG signals achieved 94.7% accuracy in classifying obstructive sleep apnea severity, outperforming conventional machine learning models.
May support ECG-based automated OSA screening; leaves open prospective validation before clinical adoption.
Background/Objectives: Obstructivesleep apnea (OSA) is one of the most widespread forms of sleep disease, affecting over 936 million adults globally. The health consequences of obstructive sleep apnea (OSA) are well documented; however, it remains largely underdiagnosed because the current gold-standard diagnostic method, polysomnography (PSG), is often costly, time-consuming, and unavailable in many healthcare settings. To address these challenges, this study presents a novel explainable deep learning (DL) framework for automated multi-class OSA severity classification using single-lead electrocardiogram (ECG) signals. Methods: The proposed framework integrates a hybrid CNN–BiLSTM architecture with explainable artificial intelligence (XAI) techniques to generate clinically meaningful predictions and explanations across four OSA severity classes: Normal, Mild, Moderate, and Severe. The framework was evaluated using the publicly available PhysioNet Apnea-ECG dataset (70 recordings) together with an institutional ECG dataset (150 recordings), resulting in a combined cohort of 220 recordings. Results: The proposed framework achieved an overall classification accuracy of 94.7%, with sensitivity and specificity values of 92.3% and 96.1%, respectively. Furthermore, the proposed model consistently outperformed conventional machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and XGBoost, by 5.5%, 4.2%, and 2.9%, respectively. To enhance transparency and clinical trust, SHAP (SHapley Additive exPlanations) was employed to identify the most influential physiological predictors driving model decisions. Heart rate variability features, particularly RMSSD and pNN50, emerged as the strongest indicators of OSA severity. Moreover, computational efficiency analysis revealed that the model required only 0.23 s to process a 60 s ECG epoch on a standard computing platform, supporting its suitability for real-time deployment. Conclusions: The findings demonstrate that explainable deep learning applied to ECG signals can provide accurate, interpretable, and computationally efficient assessment of OSA severity. The proposed framework may support OSA screening, clinical triage, and early intervention, particularly in resource-constrained healthcare environments.
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Al-Quran et al. (2026) studied Obstructive sleep apnea (OSA) (n=220). Explainable CNN-BiLSTM framework vs. Conventional machine learning algorithms (e.g., XGBoost, Random Forest, SVM) was evaluated on Overall classification accuracy for four-class OSA severity (95% CI 93.9-95.4, p=<0.001). The explainable CNN-BiLSTM framework achieved an overall classification accuracy of 94.7% for four-class obstructive sleep apnea severity detection, significantly outperforming conventional models.
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