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在ARM Cortex-M4微控制器上设计和实施心房动检测算法

Marek Żyliński¹, Amir Nassibi¹, Danilo P Mandic¹

¹Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.

Sensors (Basel, Switzerland)

|September 9, 2023

概括

这项研究证明了使用微控制器在可穿戴设备上有效检测心房动. 支持矢量机分类器实现了96.9%的准确性,使得有效的边缘计算用于心律失常监测.

科学领域:

生物医学工程生物医学工程
嵌入式系统嵌入式系统
机器学习机器学习

背景情况:

中级微控制器现在可以执行边缘计算,包括神经网络计算.
这使得能够在可穿戴设备上实现信号采集,处理和机器学习的端到端解决方案.

研究的目的:

在ARM Cortex-M4微控制器上设计和实施用于检测心房的分类器.
通过使用CMSIS-DSP库来评估天真贝叶斯和支持向量机 (SVM) 分类器的性能.
为机器学习模型开发一个Python-to-C环境转移脚本.

主要方法:

利用PhysioNet/Computing在心脏病挑战2020数据进行培训和评估.
通过CMSIS-DSP库实现了Naïve Bayes和SVM分类器与各种内核.
在STM32WB55RG微控制器上测试了分类器的性能,专注于心率不规则,用于心房的分类.

主要成果:

带有辐射基函数 (RBF) 内核的SVM分类器实现了最高的准确性 (96.9%),灵敏度 (98.4%) 和特异性 (95.8%).
RBF SVM 分类器每次记录的执行时间为720μs.
边缘计算的证明优势:提高功率效率,增强数据隐私,降低运营成本.

结论:

微控制器上的边缘计算可用于实时心律失常检测.
RBF SVM 分类器为可穿戴设备上的心房动检测提供了一个高度准确和高效的解决方案.
需要进一步的研究来解决错误阳性检测和设备检测到心房的临床意义.

关键词:

心房动检测检测心房动的检测边缘计算是一种边缘计算.机器学习是机器学习.可穿戴设备可穿戴设备.

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