Estimation of Heart Rate Variability Parameters by Machine Learning Approaches Applied to Facial Infrared Thermal Imaging.
cardiovascular risk assessment
heart rate variability
infrared thermography
machine learning
remote sensing
support vector machine
Journal
Frontiers in cardiovascular medicine
ISSN: 2297-055X
Titre abrégé: Front Cardiovasc Med
Pays: Switzerland
ID NLM: 101653388
Informations de publication
Date de publication:
2022
2022
Historique:
received:
10
03
2022
accepted:
04
04
2022
entrez:
3
6
2022
pubmed:
4
6
2022
medline:
4
6
2022
Statut:
epublish
Résumé
Heart rate variability (HRV) is a reliable tool for the evaluation of several physiological factors modulating the heart rate (HR). Importantly, variations of HRV parameters may be indicative of cardiac diseases and altered psychophysiological conditions. Recently, several studies focused on procedures for contactless HR measurements from facial videos. However, the performances of these methods decrease when illumination is poor. Infrared thermography (IRT) could be useful to overcome this limitation. In fact, IRT can measure the infrared radiations emitted by the skin, working properly even in no visible light illumination conditions. This study investigated the capability of facial IRT to estimate HRV parameters through a face tracking algorithm and a cross-validated machine learning approach, employing photoplethysmography (PPG) as the gold standard for the HR evaluation. The results demonstrated a good capability of facial IRT in estimating HRV parameters. Particularly, strong correlations between the estimated and measured HR (
Identifiants
pubmed: 35656402
doi: 10.3389/fcvm.2022.893374
pmc: PMC9152459
doi:
Types de publication
Journal Article
Langues
eng
Pagination
893374Informations de copyright
Copyright © 2022 Di Credico, Perpetuini, Izzicupo, Gaggi, Cardone, Filippini, Merla, Ghinassi and Di Baldassarre.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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