  Monitoring electrocardiogram signals is of great significance for the
diagnosis of arrhythmias. In recent years, deep learning and convolutional
neural networks have been widely used in the classification of cardiac
arrhythmias. However, the existing neural network applied to ECG signal
detection usually requires a lot of computing resources, which is not friendlyF
to resource-constrained equipment, and it is difficult to realize real-time
monitoring. In this paper, a binarized convolutional neural network suitable
for ECG monitoring is proposed, which is hardware-friendly and more suitable
for use in resource-constrained wearable devices. Targeting the MIT-BIH
arrhythmia database, the classifier based on this network reached an accuracy
of 95.67% in the five-class test. Compared with the proposed baseline
full-precision network with an accuracy of 96.45%, it is only 0.78% lower.
Importantly, it achieves 12.65 times the computing speedup, 24.8 times the
storage compression ratio, and only requires a quarter of the memory overhead.
