基于心跳间隔的可穿戴设备上的低功耗,实时心房动检测的边缘AI方法
Eliana Cinotti1, Maria Gragnaniello1, Salvatore Parlato1
1Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21, 80125 Naples, Italy.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究表明,微控制器上的神经网络可以使用心律数据准确检测心房动 (AF). 这可以在可穿戴设备上实时,低功耗的AF选.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 心房动 (AF) 是一种常见的心律障碍,需要及早检测才能有效治疗.
- 可穿戴设备可以捕捉心律信号,但复杂的AF检测算法通常需要大量的计算资源.
- 开发用于设备内AF检测的高效算法对于广泛的个人健康监测至关重要.
研究的目的:
- 调查直接在微控制器上使用神经网络算法的可行性,以实时检测心房动.
- 为了评估自定义的1D卷积神经网络 (1D-CNN) 用于使用节拍间隔 (RR) 进行AF识别的性能.
- 评估用于AF检测的边缘AI原型的性能和资源消耗.
主要方法:
- 从公开的心电图数据库中提取了RR序列 (25,50,100间隔) 与注释的AF情节.
- 设计并验证了一种1D-CNN,使用5倍的主体智能交叉验证进行主体独立评估.
- 开发了一个带有ECG前端,微控制器和物联网模块的Edge AI原型,用于现实世界的测试.
主要成果:
- 在AF检测中,1D-CNN实现了高测试准确度 (高达0.980±0.023),性能随着更长的RR序列而改善.
- 独立于主体的评估证实了强大的概括,具有高精度,回忆,F1得分和AUC-ROC.
- 边缘AI原型展示了非常低功耗的实时AF识别,适用于可穿戴应用.
结论:
- 神经网络算法可以有效地部署在微控制器上,用于实时,低功率的心房动检测.
- 拟议的方法提供了一个强大的解决方案,用于使用各种生理信号进行个人AF查.
- 该系统促进了早期AF检测,并可以传输可疑的心电图痕迹,以便医生验证.
关键词:
心房动是心房动的一种.诊断 诊断 诊断 诊断 诊断 诊断边缘人工智能 边缘人工智能心率的心跳是如何发生的.低功率的低功率电机微控制器上的微控制器神经网络的神经网络的神经网络穿戴式设备可以穿戴.更多相关视频
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