Why the study?
Wearable Artificial Intelligence-of-Things devices exhibit the need to be resource and energy-efficient.
A low-complexity binarized 2D-CNN classifier for wearable ECG devices demonstrates high classification accuracy with significantly reduced computational and power requirements.
May enable efficient wearable ECG monitoring; leaves open prospective clinical validation.
Wearable Artificial Intelligence-of-Things (AIoT) devices exhibit the need to be resource and energy-efficient. In this paper, we introduced a quantized multilayer perceptron (qMLP) for converting ECG signals to binary image, which can be combined with binary convolutional neural network (bCNN) for classification. We deploy our model into a low-power and low-resource field programmable gate array (FPGA) fabric. The model requires 5.8× lesser multiply and accumulate (MAC) operations than known wearable CNN models. Our model also achieves a classification accuracy of 98.5%, sensitivity of 85.4%, specificity of 99.5%, precision of 93.3%, and F1-score of 89.2%, along with dynamic power dissipation of 34.9 μW.
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Wong et al. (2022) studied ECG signal classification. Quantized multilayer perceptron (qMLP) combined with binary convolutional neural network (bCNN) vs. Known wearable CNN models was evaluated on Classification accuracy.
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