  Reasonably and effectively monitoring arrhythmias through ECG signals has
significant implications for human health. With the development of deep
learning, numerous ECG classification algorithms based on deep learning have
emerged. However, most existing algorithms trade off high accuracy for complex
models, resulting in high storage usage and power consumption. This also
inevitably increases the difficulty of implementation on wearable Artificial
Intelligence-of-Things (AIoT) devices with limited resources. In this study, we
proposed a universally applicable ultra-lightweight binary neural network(BNN)
that is capable of 5-class and 17-class arrhythmia classification based on ECG
signals. Our BNN achieves 96.90% (full precision 97.09%) and 97.50% (full
precision 98.00%) accuracy for 5-class and 17-class classification,
respectively, with state-of-the-art storage usage (3.76 KB and 4.45 KB).
Compared to other binarization works, our approach excels in supporting two
multi-classification modes while achieving the smallest known storage space.
Moreover, our model achieves optimal accuracy in 17-class classification and
boasts an elegantly simple network architecture. The algorithm we use is
optimized specifically for hardware implementation. Our research showcases the
potential of lightweight deep learning models in the healthcare industry,
specifically in wearable medical devices, which hold great promise for
improving patient outcomes and quality of life. Code is available on:
https://github.com/xpww/ECG_BNN_Net

Signal Processing;Machine Learning