Prediction of Atrial Fibrillation using artificial intelligence on Electrocardiograms: A systematic review

Atrial Fibrillation (AF) is a type of arrhythmia characterized by irregular heartbeats, with four types, two of which are complicated to diagnose using standard techniques such as Electrocardiogram (ECG). However, and because smart wearables are increasingly a piece of commodity equipment, there are several ways of detecting and predicting AF episodes using only an ECG exam, allowing physicians easier diagnosis. By searching several databases, this study presents a review of the articles published in the last ten years, focusing on those who reported studies using Artificial Intelligence (AI) for prediction of AF. The results show that only twelve studies were selected for this systematic review, where three of them applied deep learning techniques (25%), six of them used machine learning methods (50%) and three others focused on applying general artificial intelligence models (25%). To conclude, this study revealed that the prediction of AF is yet an under-developed field in the context of AI, and deep learning techniques are increasing the accuracy, but these are not as frequently applied as it would be expected. Also, more than half of the selected studies were published since 2016, corroborating that this topic is very recent and has a high potential for additional research. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

This study reviewed articles published in the last ten years on predicting atrial fibrillation using artificial intelligence. It found that deep learning techniques can improve accuracy, but are not as widely used as expected. The research also revealed that the field of AI for prediction of AF is still in its early stages, with high potential for further study.