基于生理模型的神经网络,用于建模体育活动期间的代谢-心率关系
Yaowen Zhang1, Libera Fresiello2, Peter H Veltink1
1Department of Biomedical Signals and Systems, University of Twente, Enschede, 7522NB, The Netherlands.
Computer methods and programs in biomedicine
|January 13, 2026
概括
一个新的基于生理模型的神经网络 (PMB-NN) 准确地估计身体活动期间的心率 (HR). 这一进步使个性化的心脏监测能够用于早期发现心力衰竭 (HF).
科学领域:
- 生物医学工程 生物医学工程
- 生理学建模 生理学建模
- 机器学习用于医疗保健
背景情况:
- 心力衰竭 (HF) 是一个全球性的健康问题,早期检测对于更好的患者结果至关重要.
- 在日常活动中心率异常 (HR) 可以表明HF风险,但当前的监测依赖于人口平均值.
- 个性化HR估计为跟踪心脏健康生物标志物提供了个性化的方法.
研究的目的:
- 引入一种新的基于生理模型的神经网络 (PMB-NN) 框架,用于建模氧气吸收 (V ̇O2) -HR关系.
- 建立一个基于生理学的模块,用身体运动信号来估计日常生活HR.
- 为了实现精确的,个性化的心脏监测,以提高HF风险检测.
主要方法:
- 开发了一个PMB-NN框架,从简化的代谢-HR生理模型 (PM) 中嵌入生理约束.
- 在各种体力活动期间,对25名参与者的个人数据集进行了培训和测试.
- 对数值准确性,生理学可信性和可解释性进行评估的PMB-NN与基准模型 (FCNN,PM) 相比.
主要成果:
- PMB-NN表现出高估计准确度 (中位数R2=0.88,RMSE=9.96bpm) 和遵守生理学原则.
- 实现了与FCNN可比的性能,并且在精度方面明显优于PM (p<0.001).
- 与FCNN (p=0.028) 和PM (p<0.001) 相比,显示出更高的生理合理性 (ρ=1),在输入不确定性下具有强大的性能.
结论:
- 通过代谢输入验证了PMB-NN框架,以使用代谢输入进行准确的HR估计.
- 该框架的基础验证支持未来与可穿戴V̇O2估计系统的整合.
- 能够在日常活动中进行个性化,实时的心脏监测,以改进高频风险检测.
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