Machine learning techniques for arrhythmic risk stratification: a review of the literature

Abstract

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians' unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

Keywords: Artificial intelligence; Machine learning; Prediction models; Risk stratification; Ventricular arrhythmias; Ventricular fibrillation; Ventricular tachycardia.

Fig. 1

First decision tree of the random forest model predicting risk of atrial fibrillation [15]

Fig. 2

Seven-layered optimal architecture of the CNN model predicting risk of atrial fibrillation [16]

Fig. 3

LSTM recurrent network architecture detecting arrhythmia on imbalanced ECG datasets [17]