Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.
Journal:
Journal of cardiovascular translational research
PMID:
39017912
Abstract
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 of 0.955. This novel ML algorithm could assist clinicians in the care of HF patients.
Authors
Keywords
Animals
Disease Models, Animal
Electrocardiography
Feasibility Studies
Heart Failure
Heart Rate
Heart Sounds
Machine Learning
Oximetry
Phonocardiography
Predictive Value of Tests
Signal Processing, Computer-Assisted
Stroke Volume
Swine
Swine, Miniature
Ventricular Function, Left
Ventricular Pressure