Sleep Apnea Detection Using Wearable ECG and Deep Learning: Validation with Polysomnography

Abstract Obstructive sleep apnea (OSA) is a highly prevalent disorder that remains underdiagnosed due to the limited accessibility and high cost of polysomnography (PSG), the current diagnostic standard. This study presents a deep learning model that detects OSA from single-lead electrocardiogram (ECG) signals acquired via a wearable patch Holter device. We collected ECG data from 92 adult patients undergoing overnight PSG at a sleep clinic. A 1-dimensional dilated convolutional neural network (1D-CNN) was trained using 3-minute ECG segments and validated against PSG-derived apnea-hypopnea index (AHI) scores. The model achieved an accuracy of 81.1%, precision of 81.9%, recall of 82.5%, F1-score of 82.2%, and an area under the receiver operating characteristic curve (AUROC) of 0.875. The predicted AHI was strongly correlated with PSG AHI (r = 0.847), and the model outperformed conventional screening questionnaires such as STOP-Bang in identifying moderate-to-severe OSA (AUROC = 0.888). These results demonstrate the feasibility of using wearable ECG and deep learning for accurate and scalable OSA detection. This approach may offer a non-invasive and cost-effective alternative to PSG in both clinical and community-based screening settings.