Complex AFNet: A Hybrid Complex-Valued Deep Network for Atrial Fibrillation Detection

This research presents a new deep learning model called Complex AFNet that detects atrial fibrillation (AF, an irregular heartbeat condition affecting millions worldwide) from electrocardiogram (ECG, a recording of heart electrical activity) signals. The model converts ECG data into 2D images using mathematical transformations and uses complex-valued convolutions (a type of neural network layer that processes numbers with both magnitude and phase information) to identify subtle patterns that distinguish normal heartbeats from AF episodes, achieving 95.63% accuracy on a standard medical dataset.