Atrial Fibrillation Detection Based on Deep Learning Models

Abstract

Atrial fibrillation is the most common sustained heart rhythm abnormality in clinical practice that can lead to well-known medical complications associated with increased mortality. The diagnosis of atrial fibrillation can be detected by using a short electrocardiogram recording (ECG). However, because the heartbeat is irregular and does not constantly present on a simple electrocardiogram, a single diagram is not enough for a final and certain diagnosis. To monitorizing one single patient requires hours of monitoring, important costs, and low yield. Our aim is to develop a rapid, inexpensive way to identify patients with atrial fibrillation using neural networks. This study serves to help the clinician with an automatic approach to give a quick and safe diagnosis for each patient population. The experiments show that this approach offers a promising atrial fibrillation classification and outperforms recently published studies that either use extracted features or raw data separately.