A Multi‐Branch Attention‐Enhanced Architecture for OSA Detection Using ECG Signals

Obstructive Sleep Apnea (OSA) is a prevalent and underdiagnosed sleep disorder that can lead to serious cardiovascular and cognitive complications if left untreated. This study presents a novel deep learning architecture based on convolution, LSTM, short‐time Fourier transform (STFT), attention, and transformer modules for automated OSA detection using single‐lead electrocardiogram (ECG) signals, aiming to improve diagnostic accuracy. The proposed model integrates six parallel branches for feature extraction, combining convolutional layers, recurrent units, STFT, and residual connections to capture multiscale temporal and frequency‐domain patterns. A three‐path feature refinement that incorporates sequential, convolutional, and transformer encoder is considered as the second part of the model. Channel attention‐based feature fusion modules are employed in the first and second parts to enhance feature relevance and suppress noise. Experimental evaluations on the PhysioNet Apnea‐ECG dataset demonstrate that the proposed model achieves superior segment‐level classification performance with 91.97% accuracy, 91.28% sensitivity, and 92.41% specificity. These findings suggest that the proposed method offers a robust, and scalable solution. Regarding the small number of parameters of the model, it can potentially be considered for real‐time and wearable‐based OSA monitoring applications. All codes and the trained model are released at https://github.com/mziaratban/OSA .