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New TF-IG Framework Improves Heart Rate Estimation from PPG Signals

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new parameter-efficient framework called TF-IG for estimating heart rate from photoplethysmography (PPG) signals. This method combines temporal waveform analysis with frequency-domain periodicity extraction through a dual-branch architecture. An interaction-gated fusion module balances these two domains, leading to improved accuracy and efficiency. Experiments on the BIDMC PPG and Respiration Dataset demonstrated TF-IG's effectiveness, achieving a mean absolute error of 1.227 BPM with only 84.6K parameters. AI

IMPACT This research offers a more efficient method for heart rate estimation using PPG signals, potentially improving wearable health monitoring devices.

RANK_REASON The cluster contains a research paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New TF-IG Framework Improves Heart Rate Estimation from PPG Signals

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The cluster contains a research paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Anh Khoi Pham, Phat Lam, Thien Truong Nguyen Ly, Lam Pham ·

    Interaction-Gated Time-Frequency Learning for Parameter-Efficient PPG-Based Heart Rate Estimation

    arXiv:2610.28693v1 Announce Type: new 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 fre…

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