从心电图和HRV根据多类SVM对运动疲劳水平的分类
Yuru Chen1, Huanmin Ge2, Xinhua Su1
1School of Sports Engineering, Beijing Sport University, Beijing, China.
Medical & biological engineering & computing
|May 5, 2024
概括
这项研究引入了一种混合方法,用于使用心电图 (ECG) 图像和心率变化 (HRV) 特征的深度学习来分类运动疲劳. 该方法提高了准确性,并减少了实时疲劳检测计算时间.
科学领域:
- 生理学 生理学 生理学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 运动疲劳分类对于优化训练和防止过度劳累至关重要.
- 心电图 (ECG) 和心率变化 (HRV) 是监测疲劳的有价值的生理信号.
- 目前的疲劳分类方法可能缺乏准确性或实时适用性.
研究的目的:
- 为准确和及时的运动疲劳分类开发一种新的混合方法.
- 将来自心电图像的深度神经网络特征与线性HRV特征相结合.
- 评估拟议方法在公开和自主收集的数据集上的表现.
主要方法:
- 电脑心电图信号被转化为二维图像,使用短时间里埃转换 (STFT).
- 使用视觉几何组 (VGG) 深度学习模型提取图像特征.
- 组合ECG图像特征和线性HRV特征被输入到各种分类器中.
主要成果:
- 混合方法在两个数据集上都实现了高准确度,灵敏度和F1分数 (例如,EPHNOGRAM的准确度为96.90%).
- 与单个特征集相比,连接的特征表现出了优越的分类性能.
- 系统的计算时间大大缩短,使实时应用成为可能.
结论:
- 拟议的混合方法通过整合深度学习和HRV分析,有效地对运动疲劳进行分类.
- 这种方法为实时运动疲劳监测提供了更好的准确性和及时性.
- 这些发现表明,它对运动员,教练和医疗保健专业人员来说是一个有希望的工具.
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