Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation

Studies non-linear chaotic HR behavior via mutual information; proposes approach that explains/handles temporal complexity and improves DL HR estimators when combined. Validated on four established real-life datasets with ablations; claims up to 40% improvement vs traditional / existing ML while reducing multi-sensor reliance and post-processing (project abstract).

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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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