Photoplethysmography (PPG)
37 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
These leaderboards are used to track progress in Photoplethysmography (PPG)
Libraries
Use these libraries to find Photoplethysmography (PPG) models and implementations
UBFC-rPPG
MMSE-HR
MTHS
MIMIC PERform Testing Dataset
Subtasks
Most implemented papers
Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks
psu1/DeepRNN
•
•
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.
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.
KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch
esl-epfl/KID-PPG
•
•
Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation.
Deep Imbalanced Regression to Estimate Vascular Age from PPG Data: a Novel Digital Biomarker for Cardiovascular Health
Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human hemodynamics, with recent studies highlighting its potential in assessing vascular aging through deep learning.
End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables
We present a convolutional-recurrent neural network architecture with long short-term memory for real-time processing and classification of digital sensor data.
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.
An Open Framework for Remote-PPG Methods and their Assessment
phuselab/pyVHR
•
•
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.
