A novel VLSI chip using a convolutional neural network demonstrates high accuracy and efficiency for detecting premature ventricular contractions in 12-lead ECG signals.
May enable on-chip PVC detection; leaves open prospective clinical validation before practice change.
We propose a very large-scale integration (VLSI) chip for premature ventricular contraction (PVC) detection. The chip contains a convolutional neural network (CNN) for detecting the abnormal heartbeats associated with PVCs in 12-lead electrocardiogram signals. The proposed CNN comprises two convolutional layers and a fully connected layer; in testing, it achieved a high PVC detection accuracy of [Formula: see text]. Created by using a [Formula: see text]-[Formula: see text]m CMOS process, the developed chip consumes [Formula: see text] mW with a clock frequency of 50 MHz and gate count of [Formula: see text] K. Compared with the previously designed VLSI chips, the proposed CNN chip achieves higher accuracy in abnormal heartbeat detection.
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Chen et al. (2021) studied this question.
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