A Machine Learning model of the combination of normalized SD1 and SD2 indexes from 24h-Heart Rate Variability as a predictor of myocardial infarction

TL;DR

This study demonstrates that machine learning models utilizing nonlinear 24-hour heart rate variability indexes, specifically SD1nu + SD2nu, can effectively predict myocardial infarction, outperforming linear domain indexes.

Contribution

The paper introduces a novel approach combining nonlinear HRV indexes with machine learning to improve MI prediction accuracy.

Findings

SD1nu + SD2nu indexes have higher predictive power for MI.

Stochastic Gradient Boosting achieved the best precision.

Nonlinear HRV indexes outperform linear domain indexes in MI prediction.

Abstract

Aim: to evaluate the ability of the nonlinear 24-HRV as a predictor of MI using Machine Learning Methods: The sample was composed of 218 patients divided into two groups (Healthy, n=128; MI n=90). The sample dataset is part of the Telemetric and Holter Electrocardiogram Warehouse (THEW) database, from the University of Rochester Medical Center. We used the most common ML algorithms for accuracy comparison with a setting of 10-fold cross-validation (briefly, Linear Regression, Linear Discriminant Analysis, k-Nearest Neighbour, Random Forest, Supporting Vector Machine, Na\"ive Bayes, C 5.0 and Stochastic Gradient Boosting). Results: The main findings of this study show that the combination of SD1nu + SD2nu has greater predictive power for MI in comparison to other HRV indexes. Conclusion: The ML model using nonlinear HRV indexes showed to be more effective than the linear domain, evidenced through the application of ML, represented by a good precision of the Stochastic Gradient Boosting model. Keywords: heart rate variability, machine learning, nonlinear domain, cardiovascular disease