心血管疾病中的TinyML和边缘智能应用:一项调查
Ali Reza Keivanimehr1, Mohammad Akbari2
1Department of Management, Science and Technology, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Computers in biology and medicine
|January 11, 2025
概括
微型机器学习 (TinyML) 能够在可穿戴设备上进行低功耗心血管监测. 这项技术优化机器学习模型,在网络边缘实时进行心脏异常分析.
科学领域:
- 边缘计算是一种边缘计算.
- 嵌入式系统 嵌入式系统
- 生物医学工程 生物医学工程
背景情况:
- 微型机器学习 (TinyML) 和边缘智能对于资源有限的设备至关重要.
- 可穿戴设备为无处不在的低功耗健康监测提供了一个平台.
- 心脏异常需要持续监测和实时分析.
研究的目的:
- 探索TinyML在使用可穿戴设备进行心血管监测方面的潜力.
- 审查TinyML启用器,网络解决方案和优化技术.
- 在边缘设备上分析深度神经网络以实时ECG分析.
主要方法:
- 对TinyML硬件和软件启用器的概述.
- 对TinyML部署的低功耗广域网 (LPWAN) 的检查.
- 讨论知识的蒸,量化和修剪,以优化模型.
- 对心电图数据的深度神经网络 (CNN,自动编码器,DBN,变压器) 的分析.
主要成果:
- TinyML促进了高效的低功耗心血管监测.
- 优化技术可以在边缘设备上实现复杂的ML模型.
- 不同的神经网络架构显示出对心电图分析的前景.
结论:
- TinyML是一种用于实时心血管监测的变革性技术.
- 高效的深度神经网络是可穿戴心脏异常检测的关键.
- 整合TinyML和边缘智能推进了医疗保健技术.
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