Atrial Fibrillation Detection with Spectrogram and Convolutional Neural Networks

Abstract

Atrial fibrillation (AF) is one of the most common heart arrhythmias and can lead to various complications such as heart failure stroke reduced exercise capacity palpitations anxiety shortness of breath and high blood pressure if not diagnosed promptly. In this study we investigated the application of time-frequency domain techniques and artificial intelligence tools for the diagnosis of AF. We proposed two custom-designed Convolutional Neural Network (CNN) architecture. 24-hour Holter ECG records from patients with AF and control subjects from the Cardiology Department of Ege University were used as dataset. Ten seconds of ECG time series signals were employed to train a 1D CNN while spectrogram images created from these signals were used to train a 2D CNN. We observed that the proposed spectrogram-2D CNN outperformed the 1D CNN benefiting from the time-frequency information extracted by the spectrogram. © 2024 Elsevier B.V. All rights reserved.