期刊
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
卷 16, 期 2, 页码 222-232出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBCAS.2022.3152623
关键词
Electrocardiography; Convolution; Pregnancy; Heart rate variability; Field programmable gate arrays; Convolutional neural networks; Image edge detection; Artificial Intelligence-of-Things; co-design; convolutional neural network; ECG; field programmable gate array; fusion; inference; low-power design; wearable; state machine
This paper presents an efficient binary convolutional neural network algorithm for classifying between Ventricular and non-Ventricular Ectopic Beat images. The algorithm achieves high classification accuracy and sensitivity, with low dynamic power dissipation.
Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-efficient. In this paper, we design and implement an efficient binary convolutional neural network (bCNN) algorithm utilizing function-merging and block-reuse techniques to classify between Ventricular and non-Ventricular Ectopic Beat images. We deploy our model into a low-resource low-power field programmable gate array (FPGA) fabric. Our model achieves a classification accuracy of 97.3%, sensitivity of 91.3%, specificity of 98.1%, precision of 86.7%, and F1-score of 88.9%, along with dynamic power dissipation of only 10.5-mu W.
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