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In this paper, we propose a data-shifting neural network (DSNN) for the detection of abnormal heartbeats. Our study aims to identify six types of electrocardiogram (ECG) signals using the deep learning network. In order to enhance the detection accuracy, the DSNN is devised by doubling the input signal using a data shifting scheme so that the amount of information for training may be adequately sufficient. Although the computational time doubles, the accuracy can be improved. When implemented using the Taiwan Semiconductor Manufacturing Company (TSMC) 0.18-&#x03BC; m  complementary metal oxide semiconductor (CMOS) process, the proposed DSNN chip has an operating frequency at 20 MHz with chip area of 0.619 mm  2  and maximum power dissipation 0.75 mW . As a result, the proposed DSNN can substantially increase detection accuracy for the task of ECG heartbeat classification. Results obtained after applying the proposed circuit to the ECG signals drawn from the MIT-BIH arrhythmia database showed that it achieved a detection rate of 97.17% with a small chip area, suggesting that it may be suitable for wearable or portable devices in healthcare.

A Very Large-Scale Integration (VLSI) Chip Design for Abnormal Heartbeat Detection Using a Data-Shifting Neural Network (DSNN)