在分布式边缘设备上实时心跳分类:性能和资源利用研究
Eko Sakti Pramukantoro1, Kasyful Amron1, Putri Annisa Kamila2
1Faculty of Computer Science, Universitas Brawijaya, Malang 65145, Indonesia.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了使用可穿戴传感器和机器学习进行早期心脏病检测的实时系统. 它在心跳分类中达到99%的准确性,并具有快速推断时间,可立即诊断.
科学领域:
- 心脏病学和卫生技术技术
- 机器学习和分布式计算
背景情况:
- 早期发现心脏病对于预防至关重要.
- 可穿戴设备和机器学习为自动心跳检测提供了潜力.
- 现有的心跳分类系统经常使用批处理,导致延迟.
研究的目的:
- 开发使用分布式流处理的实时心跳分类推理系统.
- 为了实现早期诊断,对心电图数据进行持续,即时和可扩展的分析.
- 为了解决当前系统中批处理的局限性.
主要方法:
- 实现了一个实时系统与分布式流处理和Flask框架.
- 通过蓝牙和Web蓝牙API集成的Polar H10传感器用于ECG数据采集.
- 使用了具有RR间隔,形态和波形特征的LSTM-512,LSTM-256和FCN模型.
主要成果:
- 在心跳分类中达到99%的准确性,具有最佳的波形特征和LSTM-Sequential架构.
- 演示实时性能,推断时间为0.12秒.
- 在Jetson Orin设备上展示了高效的资源利用率 (24.75%的CPU,0.34%的GPU,293MB的内存).
结论:
- 实时心跳分类在使用优化的功能和模型的分布式边缘设备上是可行的.
- 开发的分布式架构确保了持续ECG监测的弹性和可扩展性.
- 该系统通过立即数据分析,增强了对心脏病的早期诊断能力.
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