Key result
A novel YOLO-based 1D CNN algorithm demonstrated high performance in speed and mean average precision for detecting and classifying arrhythmias from long-duration ECG signals.
Why the study?
Satisfactory algorithms for real-time monitoring and early detection of arrhythmias in wearable devices are lacking.
Does a YOLO-based 1D CNN algorithm improve the speed and precision of arrhythmia detection in long-duration ECG signals?
Does a YOLO-based 1D CNN algorithm improve the speed and precision of arrhythmia detection in long-duration ECG signals?
A novel YOLO-based 1D CNN algorithm enables fast and precise real-time arrhythmia detection from raw ECG signals, making it suitable for wearable devices.
May support wearable arrhythmia monitoring development; leaves open prospective clinical validation and outcome impact.
Early detection of arrhythmias is very important. Recently, wearable devices are being used to monitor the patient’s heartbeat to detect an arrhythmia. However, there are not satisfactory algorithms for real-time monitoring of arrhythmias 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 electrocardiogram (ECG) signals without R-peak detection and can classify an arrhythmia simultaneously. The model replaces the 2D Convolutional Neural networks (CNN) with a 1D CNN and the bounding box with a bounding window to utilize raw ECG signals. Results demonstrate that the proposed algorithm has high performance in speed and mean average precisionin detecting an arrhythmia. Furthermore, the bounding window can predict different window lengths on different types of arrhythmia. Therefore, the model can choose an 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.
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Hwang et al. (2020) studied Arrhythmias. YOLO-based 1D CNN algorithm was evaluated on Arrhythmia detection and classification (speed and mean average precision). A novel YOLO-based 1D CNN algorithm demonstrated high performance in speed and mean average precision for detecting and classifying arrhythmias from long-duration ECG signals.
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