[Submitted on 20 Oct 2026]
Title:Interaction-Gated Time-Frequency Learning for Parameter-Efficient PPG-Based Heart Rate Estimation
View a PDF of the paper titled Interaction-Gated Time-Frequency Learning for Parameter-Efficient PPG-Based Heart Rate Estimation, by Anh Khoi Pham and 3 other authors
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Abstract:Photoplethysmography (PPG) enables low-cost and non-invasive heart rate (HR) monitoring but remains sensitive to waveform distortion, physiological variability, and measurement interference. Temporal waveform characteristics and frequency-domain periodicity provide complementary information for HR estimation; however, effectively exploring and integrating these representations within a compact model remains challenging. This paper proposes TF-IG, a parameter-efficient interaction-gated time-frequency learning framework for PPG-based HR estimation. TF-IG employs a dual-branch architecture in which a temporal branch captures pulse morphology via dilated depthwise-separable convolutions, while a frequency branch extracts cardiac periodicity information from bounded log-spectrogram representations. To combine the two domains, we introduce an interaction-gated fusion module that explicitly models feature agreement and discrepancy, while also learning feature-wise weights to balance temporal and spectral information. A parameter-shared regression head further maps the temporal, spectral, and fused representations into the final HR prediction space. Experiments on the benchmark BIDMC PPG and Respiration Dataset using a subject-exclusive training, validation, and test protocol show that TF-IG achieves a mean absolute error (MAE) of 1.227 BPM, a root mean square error (RMSE) of 2.060 BPM, a Pearson correlation coefficient of 0.9934, and 92.28 percent of estimates within 3 BPM, while remaining parameter-efficient with only 84.6K parameters. Ablation results further demonstrate the complementary contributions of the temporal and frequency branches, interaction-gated fusion, and shared regression head. These results indicate that explicitly modeling cross-domain interactions provides an effective accuracy-complexity trade-off for lightweight PPG-based HR estimation.
Comments: Conference preprint, accepted at SoICT 2026
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
Cite as: arXiv:2610.28693 [cs.LG]
(or arXiv:2610.28693v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.28693
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Phat Lam [view email] [v1] Tue, 20 Oct 2026 09:10:28 UTC (612 KB)
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