Photoplethysmography (PPG)
11 papers with code • 0 benchmarks • 4 datasets
Photoplethysmography (PPG) is a non-invasive light-based method that has been used since the 1930s for monitoring cardiovascular activity.
Benchmarks
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Libraries
Use these libraries to find Photoplethysmography (PPG) models and implementations
UBFC-rPPG
MMSE-HR
MTHS
MIMIC PERform Testing Dataset
Subtasks
Most implemented papers
Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices
The user's pulse wave is then used to determine stress (according to the Baevsky Stress Index), heart rate, and heart rate variability.
Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks
psu1/DeepRNN
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Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics.
Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable
In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable.
HeartBEAT: Heart Beat Estimation through Adaptive Tracking
In this paper, we propose an algorithm denoted as HeartBEAT that tracks heart rate from wrist-type photoplethysmography (PPG) signals and simultaneously recorded three-axis acceleration data.
Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes
The database, containing PWs from 4, 374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data.
BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data
Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities.
An Open Framework for Remote-PPG Methods and their Assessment
phuselab/pyVHR
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This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG).
Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices
Our most accurate quantized network achieves 4. 41 Beats Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of 47. 65 mJ and a memory footprint of 412 kB.
pyVHR: a Python framework for remote photoplethysmography
phuselab/pyVHR
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PeerJ Computer Science 2022
A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades.
Video-based Remote Physiological Measurement via Self-supervised Learning
yuezijie/video-based-remote-physiological-measurement-via-self-supervised-learning
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Video-based remote physiological measurement aims to estimate remote photoplethysmography (rPPG) signals from human face videos and then measure multiple vital signs (e. g. heart rate, respiration frequency) from rPPG signals.
