Photoplethysmography (PPG)

6 papers with code • 0 benchmarks • 1 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

Datasets


Most implemented papers

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

psu1/DeepRNN 12 May 2017

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

rymc/PPAW 25 Jun 2018

In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable.

HeartBEAT: Heart Beat Estimation through Adaptive Tracking

olivesgatech/HeartBEAT 19 Oct 2018

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

peterhcharlton/pwdb 24 Oct 2019

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 26 Nov 2020

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

embeddedml-edagroup/q-ppg 24 Mar 2022

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.