Sleep Apnea Detection from ECG Signals

•Developed and compared multiple models including ResNet, Transformer, LightGBM, and CNN-LSTM-Fuzzy for binary and multi-class apnea classification.

•Worked with two real-world ECG datasets: PhysioNet Apnea-ECG and MIT-BIH.

•Designed a Flutter-based mobile application targeting medical professionals, enabling offline ECG data analysis, result visualization, and patient history tracking.