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Prediction of Cardiac Mechanical Performance From Electrical Features During Ventricular Tachyarrhythmia Simulation
Da Un Jeong1, Ki Moo Lim1,2
1Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea.
Frontiers in Physiology
|December 17, 2020
Summary
This study used machine learning models to predict cardiac mechanical contractility from electrical instability during ventricular tachyarrhythmia. An artificial neural network with three hidden layers showed optimal predictive performance.
Area of Science:
- Computational Biology and Physiology
- Cardiac Electrophysiology
- Machine Learning in Medicine
Background:
- Ventricular tachyarrhythmia involves electrical instability features linked to mechanical contractility.
- Limited research exists on estimating mechanical contractility from electrical data during ventricular tachyarrhythmia using stochastic models.
Purpose of the Study:
- To predict cardiac mechanical performance using machine learning algorithms based on electrical instability features during ventricular tachyarrhythmia simulations.
- To compare the predictive performance of Support Vector Regression (SVR) and Artificial Neural Network (ANN) models.
Main Methods:
- Conducted an electromechanical tachyarrhythmia simulation.
- Extracted 12 electrical instability features and two mechanical properties (stroke volume, myocardial tension amplitude).
- Evaluated SVR with different kernel types and ANN with varying hidden layers.
Main Results:
- SVR models achieved highest accuracy for stroke volume with a polynomial kernel and for myocardial tension amplitude with a linear kernel.
- The ANN model demonstrated superior predictive performance compared to the SVR model.
- Optimal prediction accuracy for the ANN model was achieved with three hidden layers.
Conclusions:
- An ANN model with three hidden layers is proposed as the optimal approach for predicting cardiac mechanical contractility during ventricular tachyarrhythmia.
- Findings may enable indirect estimation of hemodynamic response from optical mapping data during surgery and assess contractility under normal conditions.
