Subject: ECG waveform, Electrocardiogram, Artificial Intelligence, Prediction algorithms, Atrial Fibrillation
Year: 2021
Type: Article
Title: Prediction of Atrial Fibrillation using artificial intelligence on Electrocardiograms: A systematic review
Author: Matias, Igor
Author: Garcia, Nuno
Author: Pirbhulal, Sandeep
Author: Felizardo, Virginie
Author: Pombo, Nuno
Author: Zacarias, Henriques
Author: Sousa, Miguel
Author: Zdravevski, Eftim
Abstract: 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.
Publisher: Elsevier
Relation: Computer Science Review
Identifier: oai:repository.ukim.mk:20.500.12188/20981
Identifier: http://hdl.handle.net/20.500.12188/20981
