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medical-10

Photoplethysmography (PPG)

3260 papers • 126 benchmarks • 313 datasets

Photoplethysmography (PPG) is a non-invasive light-based method that has been used since the 1930s for monitoring cardiovascular activity. Source: Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in photoplethysmography-ppg-40

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Libraries

i

Use these libraries to find photoplethysmography-ppg-40 models and implementations

2 papers 394

Datasets

UBFC-rPPG

MMSE-HR

MTHS

MIMIC PERform Testing Dataset

MIMIC PERform Testing Dataset

Subtasks

Heart rate estimation Blood pressure estimation Photoplethysmography (PPG) heart rate estimation Photoplethysmography (PPG) beat detection

Most implemented papers

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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Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

0

Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable

I. Craddock, Ryan McConville, R. Piechocki, Raúl Santos-Rodríguez, G. Archer, H. J. T. Horst, James Pope•Sun Jun 24 2018

This paper studies the prediction of heart rate from acceleration using a wrist worn wearable and proposes an online approach to tackle the concept as time passes, which can achieve good predictive performance while using the PPG heart rate sensor infrequently.

13 0

End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables

Igor Gotlibovych, Stuart Crawford, Dileep Goyal, Jiaqi Liu, Yaniv Kerem, D. Benaron, Defne Yilmaz, G. Marcus, Yihan Li•Thu Jul 26 2018

This work uses a prototype multi-sensor wearable device to collect over 180h of photoplethysmography data sampled at 20Hz and end-to-end learning to achieve state-of-the-art results in detecting AFib from raw PPG data, bringing large-scale atrial fibrillation screenings within imminent reach.

46 0

HeartBEAT: Heart beat estimation through adaptive tracking

Dogancan Temel, G. Al-Regib, Huijie Pan•Sun Jan 31 2016

Compared to state-of-the-art algorithms, HeartBEAT not only produces comparable Pearson's correlation and mean absolute error, but also higher Spearman's ρ and Kendall's τ.

21 0

Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes

P. Charlton, J. Mariscal Harana, S. Vennin, Ye Li, P. Chowienczyk, J. Alastruey•Thu Aug 22 2019

The publicly available PW database is a valuable resource for understanding CV determinants of PWs and for the development and preclinical assessment of PW analysis algorithms.

213 0

An Open Framework for Remote-PPG Methods and Their Assessment

Vittorio Cuculo, Giuseppe Boccignone, Alessandro D'amelio, G. Grossi, Raffaella Lanzarotti, Donatello Conte•Tue Dec 31 2019

Surprisingly, performances achieved by the four best rPPG methods, namely POS, CHROM, PCA and SSR, are not significantly different from a statistical standpoint, highlighting the importance of evaluate the different approaches with a statistical assessment.

91 0

Q-PPG: Energy-Efficient PPG-Based Heart Rate Monitoring on Wearable Devices

Simone Benatti, L. Benini, Alessio Burrello, D. J. Pagliari, Matteo Risso, E. Macii, M. Poncino•Wed Oct 20 2021

A design space exploration methodology to automatically generate a rich family of deep Temporal Convolutional Networks (TCNs) for HR monitoring, all derived from a single “seed” model, whose most accurate model sets a new state-of-the-art in Mean Absolute Error.

57 0

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.

65 0

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Photoplethysmography (PPG) | State-of-the-Art