Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.
Electronic stethoscope
HFrEF
Heart Failure
Invasive hemodynamics
Linear regression
Machine Learning
Porcine animal model
Journal
Journal of cardiovascular translational research
ISSN: 1937-5395
Titre abrégé: J Cardiovasc Transl Res
Pays: United States
ID NLM: 101468585
Informations de publication
Date de publication:
17 Jul 2024
17 Jul 2024
Historique:
received:
02
11
2023
accepted:
10
07
2024
medline:
17
7
2024
pubmed:
17
7
2024
entrez:
17
7
2024
Statut:
aheadofprint
Résumé
Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this procedure is invasive and time-consuming to the extent that physicians rather rely on non-invasive diagnostic tools. In this work, we assess the feasibility to develop a novel machine-learning (ML) approach to predict clinically relevant LVP indices. Synchronized invasive (pressure-volume tracings) and non-invasive signals (ECG, pulse oximetry, and cardiac sounds) were collected from anesthetized, closed-chest Göttingen minipigs. Animals were either healthy or had HF with reduced ejection fraction and circa 500 heartbeats were included in the analysis for each animal. The ML algorithm showed excellent prediction of LVP indices estimating, for instance, the end-diastolic pressure with a R
Identifiants
pubmed: 39017912
doi: 10.1007/s12265-024-10546-2
pii: 10.1007/s12265-024-10546-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : DFG; CRC 1470
Organisme : Deutsche Forschungsgemeinschaft
ID : Z01
Organisme : Progetti di Rilevante Interesse Nazionale
ID : 2017AXL54F_002
Informations de copyright
© 2024. The Author(s).
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