研究基于低负载可穿戴心电图监测设备的疲劳识别方法
Huiquan Wang1,2,3, Mengting Han1,2,3, Tasmia Avouka1,2,3
1School of Life Sciences, TianGong University, Tianjin 300387, China.
The Review of scientific instruments
|December 11, 2023
概括
这项研究使用可穿戴心电图 (ECG) 设备和机器学习来检测医务人员的疲劳. 开发的模型准确地识别了疲劳水平,改善了医疗保健专业人员的早期预警系统.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 机器学习应用 机器学习应用
背景情况:
- 医疗保健工作者面临显著的疲劳,特别是在高压环境,如流行病.
- 早期发现疲劳对于防止错误和确保工人的福祉至关重要.
- 现有的疲劳检测方法需要在现实应用中提高可靠性.
研究的目的:
- 开发和验证一种机器学习模型,用于识别前线医务人员的疲劳水平.
- 使用可穿戴设备的持续心电图 (ECG) 数据进行疲劳评估.
- 用心率变化 (HRV) 分析来提高疲劳检测的可靠性.
主要方法:
- 使用低负载可穿戴设备收集的连续心电图数据.
- 信号处理技术包括静止波形变换来减少运动工件.
- 在HRV特征上训练的随机森林分类模型.
- 使用ReliefF权重和冗余分析进行特征选择.
主要成果:
- 整体疲劳识别模型实现了97.9%的准确性和0.99.9的AUC.
- 四类疲劳水平识别模型的准确率达到了85.6%.
- 拟议的方法有效地识别了医疗工作人员在低功耗活动中的疲劳.
结论:
- 低负载可穿戴式心电图监测与基于ML的HRV分析相结合,是检测疲劳的可靠方法.
- 这种方法为医疗人员在苛刻的环境中提供了一个有前途的早期预警系统.
- 这些发现为在专业环境中对疲劳识别的进一步研究提供了基础.
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