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Cardiac Arrhythmia Detection in ECG Signals Using Graph Convolutional Network

According to the World Health Organization, by the year 2030, 23.6 million people will die from heart disease. Therefore, automatic arrhythmia detection is highly desirable. The techniques based on neural networks have obtained outstanding results for this problem. The present work explores arrhythmia detection with Graph Convolutional Networks and Dynamic Time Warping to align the heartbeats. This is the first work to address the problem as a single graph with the heartbeats as nodes to the best of our knowledge. The results indicate that the approach is promising with a Positive Prediction of 100% for Supraventricular ectopic heartbeat detection and a Sensibility of 100% for Ventricular ectopic heartbeat detection with a global accuracy of 90%.