Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

Rita Meziati Sabour; Yannick Benezeth

doi:10.1109/cvprw56347.2022.00239

ScienceGate Book Chapters

JOURNAL ARTICLE

Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

Rita Meziati Sabour Yannick Benezeth

Year: 2022 Journal: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pages: 2201-2209

DOI: 10.1109/cvprw56347.2022.00239

Get Full-Text PDF Get Analytical Report

Abstract

International audience

Keywords:

Photoplethysmogram Artificial intelligence Computer science RGB color model Computer vision SIGNAL (programming language) Segmentation Ground truth Pattern recognition (psychology)

Metrics

Cited By

1.86

FWCI (Field Weighted Citation Impact)

Refs

0.83

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Non-Invasive Vital Sign Monitoring

Physical Sciences → Engineering → Biomedical Engineering

ECG Monitoring and Analysis

Health Sciences → Medicine → Cardiology and Cardiovascular Medicine

Heart Rate Variability and Autonomic Control

Health Sciences → Medicine → Cardiology and Cardiovascular Medicine

Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

Abstract

Metrics

Citation History

Topics

Related Documents

FBMC Modulation Signal Detection Based on Gated Recurrent Unit

Model-based Region of Interest Segmentation for Remote Photoplethysmography

Plant Classification Based on Gated Recurrent Unit

Sentiment Analysis Based on Gated Recurrent Unit

Biometric Authentication Based on Enhanced Remote Photoplethysmography Signal Morphology