medical

Photoplethysmography (PPG) heart rate estimation

3260 papers • 126 benchmarks • 313 datasets

Estimating heart rate from the photoplethysmogram (PPG) signal

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in photoplethysmography-ppg-heart-rate-estimation

Trend

Dataset

Best Model

Actions

MIMIC PERform Testing Dataset

UBFC-rPPG

MMSE-HR

Libraries

i

Use these libraries to find photoplethysmography-ppg-heart-rate-estimation models and implementations

Datasets

MIMIC PERform Testing Dataset

Subtasks

No subtasks available.

Most implemented papers

DRNet: Decomposition and Reconstruction Network for Remote Physiological Measurement

Gongping Yang, Yuhang Dong, Yilong Yin•Sat Jun 11 2022

A Decomposition and Reconstruction Network (DRNet) focusing on the modeling of physiological features rather than noisy data, a novel cycle loss is proposed to constrain the periodicity of physiological information, and a plug-and-play Spatial Attention Block (SAB) is suggested to enhance features along with the spatial location information.

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Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices

L. Sigal, Peyman Bateni•Mon Oct 03 2022

The Beam AI SDK is introduced, which can monitor user stress through the selfie camera in real-time and demonstrate an average Pearson correlation of 0.801 in determining stress and heart rate variability, thus producing commercially useful readings to derive content decisions in apps.

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Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement

Guoying Zhao, Xiaopeng Hong, Zitong Yu, Xiaobai Li, Wei Peng•Fri Jul 26 2019

Comprehensive experiments are performed to show that the proposed method not only achieves superior performance on compressed videos with high-quality videos pair, but also generalizes well on novel data with only compressed videos available, which implies the promising potential for real-world applications.

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pyVHR: a Python framework for remote photoplethysmography

Vittorio Cuculo, Giuseppe Boccignone, Alessandro D'amelio, G. Grossi, Raffaella Lanzarotti, Donatello Conte, Edoardo Mortara•Thu Apr 14 2022

The present pyVHR framework represents a multi-stage pipeline covering the whole process for extracting and analyzing HR fluctuations and supports either the development, assessment and statistical analysis of novel rPPG methods, either traditional or learning-based, or simply the sound comparison of well-established methods on multiple datasets.

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Detecting beats in the photoplethysmogram: benchmarking open-source algorithms

Peter H. Charlton, Kevin Kotzen, E. Mejía-Mejía, P. Aston, K. Budidha, J. Mant, Callum Pettit, J. Behar, P. Kyriacou•Mon Jul 18 2022

Two PPG beat detectors denoted ‘MSPTD’ and ‘qppg’ performed best, with complementary performance characteristics, which can be used to inform the choice of PPGBeat detector algorithm.

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Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement

Xin Liu, Daniel J. McDuff, Josh Fromm, Shwetak N. Patel•Fri Jun 05 2020

This work presents a video-based and on-device optical cardiopulmonary vital sign measurement approach that leverages a novel multi-task temporal shift convolutional attention network (MTTS-CAN) and enables real-time cardiovascular and respiratory measurements on mobile platforms.

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BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation

B. U. Demirel, Valentin Bieri, Paul Streli, Christian Holz•Mon Jun 12 2023

A novel learning-based method that achieves state-of-the-art performance on several heart rate estimation benchmarks extracted from photoplethysmography signals (PPG) by incorporating the statistical distribution of heart rate changes into a hidden Markov model.

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