Detection of Atrial Fibrillation with a Hybrid Deep Learning Model and Time-Frequency Representations

Atrial fibrillation (AF), a common cardiac arrhythmia, can lead to severe complications, emphasizing the urgent need for effective detection methods. This study proposes an automated algorithm for AF detection that combines time-frequency analysis with deep learning techniques, achieving exceptional performance across multiple public ECG datasets. The proposed system applies variational mode decomposition (VMD) to decompose non-stationary ECG signals, followed by Hilbert transform (HT) to generate time-frequency representations. These 2D maps are then processed by a deep learning model for classification. We introduce a novel architecture, SwinMobileNet, which integrates the strengths of the Swin Transformer and MobileNetV2 to effectively model both spatial and temporal features in ECG signals. An adaptive attention mechanism ensures efficient and accurate classification. The implementation of this algorithm is available at: https://anonymous.4open.science/r/SwinMobileNet1-0C3C .