Very Large-Scale Integration for Premature Ventricular Contraction Detection Using a Convolutional Neural Network

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 98.37%. Created by using a 0.18-μm CMOS process, the developed chip consumes 4.34 mW with a clock frequency of 50 MHz and gate count of 24.8 K. Compared with the previously designed VLSI chips, the proposed CNN chip achieves higher accuracy in abnormal heartbeat detection.