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:
this https URL

本研究提出了一种超轻量的二进制神经网络（BNN），该网络基于心电图信号能够实现 5 类和 17 类心律失常的分类，其实现了最小的已知存储空间，并在 17 类分类中实现了最优的准确性，证明了深度学习在医疗保健行业，特别是在可穿戴医疗设备中的潜力。

基于超轻量级二值神经网络的心律失常分类器

Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network

Objective digital data is scarce yet needed in many domains to enable
research that can transform the standard of healthcare. While data from
consumer-grade wearables and smartphones is more accessible, there is critical
need for similar data from clinical-grade devices used by patients with a
diagnosed condition. The prevalence of wearable medical devices in the diabetes
domain sets the stage for unique research and development within this field and
beyond. However, the scarcity of open-source datasets presents a major barrier
to progress. To facilitate broader research on diabetes-relevant problems and
accelerate development of robust computational solutions, we provide the
DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal
data from wearable medical devices, including a total of 27,561 days of
continuous glucose monitor data and 8,220 days of insulin pump data from 54
patients with diabetes. This dataset is useful for developing novel analytic
solutions that can reduce the disease burden for people living with diabetes
and increase knowledge on chronic condition management in outpatient settings.

提供了 DiaTrend 数据集，该数据集是由 54 个糖尿病患者的连续血糖监测数据和胰岛素泵数据组成的整体数据，可用于开发新的分析解决方案，减少糖尿病患者的疾病负担并增加外科门诊管理慢性病的知识。