Automated Atrial Fibrillation Source Detection Using Shallow Convolutional Neural Networks

Atrial fibrillation (AF) is the most frequent sustained arrhythmia diagnosed in clinical practice. Understanding its electrophysiological mechanisms requires a precise analysis of the atrial activity (AA) signal in ECG recordings. Over the years, signal processing methods have helped cardiologists in this task by noninvasively extracting the AA from the ECG, which can be carried out using blind source separation (BSS) methods. However, the robust automated selection of the AA source among the other sources is still an open issue. Recently, deep learning architectures like convolutional neural networks (CNNs) have gained attention mainly by their power of automatically extracting complex features from signals and classifying them. In this scenario, the present work proposes a shallow CNN model to detect AA sources with an automated feature extraction step overcoming the performance of other methods present in the literature.