Discrimination of vascular aging using the arterial pulse spectrum and machine-learning analysis.
Blood pressure
Machine learning
Pulse
Spectral analysis
Vascular aging
Journal
Microvascular research
ISSN: 1095-9319
Titre abrégé: Microvasc Res
Pays: United States
ID NLM: 0165035
Informations de publication
Date de publication:
01 2022
01 2022
Historique:
received:
06
07
2021
revised:
10
08
2021
accepted:
02
09
2021
pubmed:
12
9
2021
medline:
11
3
2022
entrez:
11
9
2021
Statut:
ppublish
Résumé
Aging contributes to the progression of vascular dysfunction and is a major nonreversible risk factor for cardiovascular disease. The aim of this study was to determine the effectiveness of using arterial pulse-wave measurements, frequency-domain pulse analysis, and machine-learning analysis in distinguishing vascular aging. Radial pulse signals were measured noninvasively for 3 min in 280 subjects aged 40-80 years. The cardio-ankle vascular index (CAVI) was used to evaluate the arterial stiffness of the subjects. Forty frequency-domain pulse indices were used as features, comprising amplitude proportion (C
Identifiants
pubmed: 34508787
pii: S0026-2862(21)00110-2
doi: 10.1016/j.mvr.2021.104240
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
104240Informations de copyright
Copyright © 2021. Published by Elsevier Inc.