Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 52 references that are in the map. First posted on arXiv 31 Oct 2025.
Builds on
13 of 52 references.
References · 13
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2013Equitability, mutual information, and the maximal information coefficientcited by 45
- 2014TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercisecited by 40
- 2020CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPGcited by 34
- 2018Deep Generative Markov State Modelscited by 19
- 2015Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstructioncited by 16
- 2022Markovian Gaussian Process Variational Autoencoderscited by 16
- 2024WildPPG: A Real-World PPG Dataset of Long Continuous Recordingscited by 15
- 2019PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogramcited by 11
- 2022Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoringcited by 8
- 2024KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatchcited by 7
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Citing papers per year: 2026: 1.
1 paper.