Key result
CNN achieves highest ECG classification accuracy at ~95%, while SNN minimizes on-chip power consumption.
Why the study?
Despite high accuracy, state-of-the-art artificial neural networks for wearable ECG classification require significant computational resources and power.
In AI-based ECG classification for wearable devices, CNNs offer the highest accuracy, while SNNs provide a highly efficient alternative with the lowest power consumption and competitive accuracy.
SNNs merit testing in power-limited ECG wearables; leaves open prospective clinical validation.
Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).
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Rana et al. (2020) studied ECG classification. Artificial Neural Networks (CNN, SNN, MLP) was evaluated on Accuracy and power consumption. For ECG classification, CNN achieved the highest accuracy (95%) compared to SNN (90%) and MLP (76%), whereas SNN required the lowest on-chip power consumption (0.226 W).
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