A fine-grained convolutional recurrent model for obstructive sleep apnea detection

Obstructive Sleep Apnea (OSA) is a prevalent sleep-related breathing disorder that leads to various health issues such as hypertension, heart disease, diabetes, and stroke. In order to achieve a convenient, robust and accurate OSA detection, we analyze the cardiopulmonary coupling mechanism of OSA from single-lead electrocardiogram (ECG) signals. Then we propose a fine-grained convolutional recurrent model (FCRM) for obstructive sleep apnea detection to learn the variation of cardiopulmonary coupling (CPC) features for OSA detection. Finally, we offer interpretable insights into the model’s decisions using respiration signal and achieve fine-grained apnea classification based on attention score. The proposed model’s performance on the Apnea-ECG dataset achieved 93.2% accuracy, 89.2% sensitivity, and 96.4% specificity. This demonstrates that the method effectively extracts cardiopulmonary characteristics during sleep apnea and outperforms other methods.