优化支向量机器和自回归集成移动平均线方法,以纠正心率变化数据
Jakob Svane1, Tomasz Wiktorski1, Stein Ørn2
1Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger 4021, Norway.
运动期间的心率变化 (HRV) 分析通过新的人工物校正方法得到了改进. 自行回归集成移动平均线 (ARIMA) 和支持向量回归 (SVR) 有效地填补了由运动工件引起的数据空白.
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
- 数据科学数据科学数据科学
背景情况:
- 心率变化 (HRV) 反映了自主神经系统 (ANS) 的活动.
- 运动期间的运动在HRV数据中引入了文物,影响了分析.
- 现有的文物校正方法对于运动诱导的HRV数据是不够的.
研究的目的:
- 为了评估自回归集成移动平均 (ARIMA) 和支向量回归 (SVR) 在练习期间进行HRV人工物校正.
- 在HRV数据中确定ARIMA和SVR的最佳超参数和选择标准.
- 根据数据特征指导选择纠正方法.
主要方法:
- 应用ARIMA和SVR模型用于HRV数据人工物纠正.
- 对ARIMA和SVR模型进行超参数优化.
- 用适当的标准对模型性能进行评估,以填补差距和预测.
主要成果:
- ARIMA 和 SVR 证明了 HRV 工件纠正的潜力.
- 建立了选择最佳超参数和模型的方法.
- 为选择基于数据的校正方法提供了指导方针.
结论:
- ARIMA和SVR为身体活动期间的HRV人工物校正提供了有前途的解决方案.
- 提出的方法可以提高HRV分析在炼环境中的准确性.
- 这项工作为在动态条件下强大的HRV数据处理提供了一个框架.
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