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March 3, 2023IEEE Journal of Biomedical and Health Informatics

Learning Spatio-Temporal Pulse Representation With Global-Local Interaction and Supervision for Remote Prediction of Heart Rate

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Why the study?

Methods extracting and fusing pulse signals from multi-scale regions-of-interest suffer from heavy computational load.

Population

Two publicly available datasets and MIHR dataset

Comparison

GLISNet vs other algorithms including PhysNet and DeeprPPG

Authors

CZChangchen ZhaoMZMenghao ZhouZZZheng Zhao

Discussion

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Overview

May enable efficient multi-scale rPPG; leaves open prospective clinical validation.

Structured PICO

P
Population
Publicly available datasets (PURE, UBFC-rPPG, MIHR) for remote prediction of heart rate
I
Intervention
Global-Local Interaction and Supervision Network (GLISNet)
C
Comparator
Other algorithms (PhysNet, DeeprPPG)
O
Outcome
Signal-to-noise ratio (SNR), mean absolute error (MAE), and root mean squared error (RMSE)surrogate

The GLISNet architecture improves remote heart rate prediction accuracy and robustness, particularly in low-light environments, compared to existing algorithms.

Cite This Study

Zhao et al. (2023) studied this question.

synapsesocial.com/papers/6a70433926a7f98052dcc2d2https://doi.org/10.1109/jbhi.2023.3252091
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Also Consider

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  1. 1Noise-resilient spatial–channel refinement for robust remote photoplethysmography2026
  2. 2MSSTNet: Multi-scale facial videos pulse extraction network based on separable spatiotemporal convolution and dimension separable attention2023 · 7 citations
  3. 3Heart Rate Estimation From Facial Videos Using a Spatiotemporal Representation With Convolutional Neural Networks2020 · 158 citations
  4. 4Remote Physiological Measurement With Multiscale Feature Extraction and Global Attention Module2023 · 7 citations
  5. 5STREAM-Net: Spatio-temporal feature fusion network for robust rPPG signal measurement in remote health monitoring2025 · 4 citations