Automatic detection of Arrhythmias using a YOLO based network with Long-duration ECG signals

Early detection of arrhythmia is very important. Recently, a wearable device is used to monitor the patient’s heartbeat to detect arrhythmia. However, there are not satisfying algorithms for real-time monitoring arrhythmia in a wearable device. In this work, A novel Fast and Simple Arrhythmia detection algorithm based on YOLO is proposed. The algorithm can detect each heartbeat on long duration ECG signals without R-peak detection and can classify arrhythmia simultaneously. The model replaces the 2D CNN with 1D CNN and a bounding box with a bounding window to utilize Raw ECG signal. Results demonstrate that the proposed algorithm has high performance on speed and mAP in detecting Arrhythmia. Furthermore, the bounding window can predict different window lengths on different types of arrhythmia. Therefore, The model can choose optimal heartbeat window length for Arrhythmia classification. Since the proposed model is a compact 1D CNN model based on YOLO, it can be used in a wearable device and embedded system.