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.

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

“…In recent years, the rapid development of AI technology and rapid growth in computational power, sensor technology, and wearable devices, provide a strong basis for the diagnosis of arrhythmia 2 . AI shows great potential in all aspects of arrhythmia care, including early detection, 3 risk assessment and stratification, 4 diagnosis, 2 and treatment decision‐making 5 . This review describes and summarizes the recent development in the application of AI‐assisted electrocardiograms (ECG), photoplethysmography (PPG), and other input physiological signals for cardiac arrhythmias.…”

The Action Potential Clamp Technique as a Tool for Risk Stratification of Sinus Bradycardia Due to Loss-of-Function Mutations in HCN4: An In Silico Exploration Based on In Vitro and In Vivo Data

“…Attempts at arrhythmic risk stratification are frequently based on patient clinical parameters, including electrical history and basic and advanced electrocardiographic indices [ 9 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. However, the stratification of arrhythmic risk in patients can be difficult and controversial [ 15 , 17 , 21 , 22 , 23 ]. These days, genetic testing for the presence of gene variants is increasingly becoming part of the clinical management and risk stratification of cardiac ion channelopathies [ 6 , 15 , 24 , 25 , 26 ].…”

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

“…In addition to ML, the recent advent of deep learning-based (DL) analysis of ECGs can assist clinicians in different clinical scenarios, such as cardiovascular diseases (arrhythmias, cardiomyopathies, valve diseases, etc.) and non-cardiovascular diseases, for diagnosis, prognosis, and risk stratification [7][8][9][10][11][12]. Instead of being fed with handcrafted vectors, on which ML algorithms rely, the DL approach uses the end-toend learning strategy, which programmes the system to learn the necessary features from the raw data [13].…”