Why the study?
The study was conducted to implement a lightweight CNN-based ECG classification model with reduced parameters to enable real-time arrhythmia detection on low-power wearable devices.
A lightweight CNN model can accurately classify arrhythmias in real-time on low-power wearable devices, enabling continuous ECG monitoring.
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May support real-time arrhythmia detection on wearables; leaves open prospective clinical validation before adoption.
In this study, a lightweight CNN-based electrocardiogram (ECG) classification model is implemented to operate it on a wearable device for real-time arrhythmia detection by efficiently reducing the number of parameters of the model. Ten second-windowed ECGs from three different public ECG databases were used to learn and classify them into four classes: normal sinus rhythm, atrial fibrillation, atrial premature contraction, and ventricular premature contraction. The model implemented in the workstation environment was converted using the TensorFlow Lite framework and then imported into an ARM Cortex-M4 architecture-based nRF52840 microprocessor. The proposed model shows high performance (97.7% accuracy and 97.4% F1 score) with reasonable execution time: 298ms and current consumption: 3.55mA at optimized for speed and execution time: 480ms and current consumption: 3.82mA at optimized for size, respectively.
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Kim et al. (2022) studied this question.
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