在嵌入式边缘上检测心房动:在低功率微控制器上进行节能推理
Yash Akbari1, Ningrong Lei2, Nilesh Patel3
1School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, East Rd., Cambridge CB1 1PT, UK.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了一种人工智能系统,用于微控制器上实时检测心房动 (AF). 嵌入式边缘设备提供准确的低功耗心脏监测,增强隐私和电池寿命,用于远程患者查.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 嵌入式系统 嵌入式系统
背景情况:
- 心房动 (AF) 是一种普遍的心律失常,往往未被诊断出来,导致严重的健康风险,如中风和心力衰竭.
- 当前的AF检测方法可能需要复杂的设备或基于云的处理,这对连续的远程监控构成了挑战.
- 需要高效,低功耗的解决方案,可以直接在边缘设备上实时检测AF.
研究的目的:
- 开发和评估一种新的嵌入式边缘系统,用于实时检测心房动 (AF).
- 使用优化的人工智能模型,证明在低功耗微控制器单元 (MCU) 上执行AF分类的可行性.
- 在传统临床环境之外实现节能,保护隐私和可扩展的心脏监测.
主要方法:
- 从RR-间隔 (RRI) 数据中提取心率变化 (HRV) 特征.
- 实现了一个紧的长短期内存 (LSTM) 模型,优化为嵌入式部署在MCU上.
- 直接在边缘设备上对AF事件进行实时分类,而无需依赖完整的ECG或云分析.
主要成果:
- 在AF检测方面实现了98.46%的整体分类准确度.
- 在143 ± 0 ms内完成推断,在目标MCU上每次推断的最低功耗为3532 ± 6 μJ.
- 证明了局部推断的可行性,使警报能够进行战略无线通信,提高隐私和电池寿命.
结论:
- 在被限制的边缘设备上可以实现临床上有意义的AF监测.
- 开发的系统为节能,保护隐私和可扩展的AF选提供了一个实用的解决方案.
- 这项工作通过实用的AI驱动边缘诊断来推进个性化和分散的心脏护理.
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