Learning Heart Rate Patterns
Continuous Heart Rate Estimation with Optical Sensors
IEEE JBHI 2025Abstract
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.
Reference
Berken Utku Demirel and Christian Holz. Learning Heart Rate Patterns for Continuous Heart Rate Estimation with Optical Sensors. In Journal of Biomedical and Health Informatics 2025 (IEEE JBHI).