AI-Powered Sleep Apnea Detection and Monitoring Using Deep Learning on ECG Signals

Sleep apnea, characterized by recurrent breathing cessation during sleep, affects millions and is linked to cardiovascular diseases and diabetes. Traditional diagnostic methods like polysomnography are costly and time-consuming. This study introduces an AI-based approach using deep learning models applied to ECG signals from the PhysioNet Apnea-ECG dataset. The proposed hybrid CNN-LSTM model demonstrates superior performance in real-time sleep apnea detection, achieving high accuracy, sensitivity, and specificity. This research contributes to more efficient and accessible diagnostic tools, potentially enhancing patient care.