用非线性动力学提升真实场景下心率估计精度
Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation
- 基于互信息分析心率非线性动态，构建新估计框架
- 在四个真实数据集上提升心率估计精度最高达40%
- 减少多模态依赖与后处理，适合可穿戴设备部署
人类心率波动具有内在的复杂性和非线性特征，通常可用数学混沌理论描述，在日常心血管健康监测中带来挑战。本文通过互信息分析心率的非线性动态，提出一种新型心率估计方法。该方法不仅从数学角度解释并处理心率的时间复杂性，还能与深度学习模型结合提升性能。我们在四个真实生活场景的数据集上验证了该方法，通过大量消融实验与现有算法进行对比。结果表明，相比传统方法和现有机器学习技术，本方法在心率估计上最高提升40%，同时降低对多传感模态的依赖，并无需后处理步骤。
原文摘要 · Abstract (English)
The oscillations of the human heart rate are inherently complex and non-linear -- they are best described by mathematical chaos, and they present a challenge when applied to the practical domain of cardiovascular health monitoring in everyday life. In this work, we study the non-linear chaotic behavior of heart rate through mutual information and introduce a novel approach for enhancing heart rate estimation in real-life conditions. Our proposed approach not only explains and handles the non-linear temporal complexity from a mathematical perspective but also improves the deep learning solutions when combined with them. We validate our proposed method on four established datasets from real-life scenarios and compare its performance with existing algorithms thoroughly with extensive ablation experiments. Our results demonstrate a substantial improvement, up to 40\%, of the proposed approach in estimating heart rate compared to traditional methods and existing machine-learning techniques while reducing the reliance on multiple sensing modalities and eliminating the need for post-processing steps.
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