A Q-transform-based deep learning model for the classification of atrial fibrillation types.

Abstract

According to the World Health Organization (WHO), Atrial Fibrillation (AF) is emerging as a global epidemic, which has resulted in a need for techniques to accurately diagnose AF and its various subtypes. While the classification of cardiac arrhythmias with AF is common, distinguishing between AF subtypes is not. Accurate classification of AF subtypes is important for making better clinical decisions and for timely management of the disease. AI techniques are increasingly being considered for image classification and detection in various ailments, as they have shown promising results in improving diagnosis and treatment outcomes. This paper reports the development of a custom 2D Convolutional Neural Network (CNN) model with six layers to automatically differentiate Non-Atrial Fibrillation (Non-AF) rhythm from Paroxysmal Atrial Fibrillation (PAF) and Persistent Atrial Fibrillation (PsAF) rhythms from ECG images. ECG signals were obtained from a publicly available database and segmented into 10-second segments. Applying Constant Q-Transform (CQT) to the segmented ECG signals created a time-frequency depiction, yielding 98,966 images for Non-AF, 16,497 images for PAF, and 52,861 images for PsAF. Due to class imbalance in the PAF and PsAF classes, data augmentation techniques were utilized to increase the number of PAF and PsAF images to match the count of Non-AF images. The training, validation, and testing ratios were 0.7, 0.15, and 0.15, respectively. The training set consisted of 207,828 images, whereas the testing and validation set consisted of 44,538 images and 44,532 images, respectively. The proposed model achieved accuracy, precision, sensitivity, specificity, and F₁ score values of 0.98, 0.98, 0.98, 0.97, and 0.98, respectively. This model has the potential to assist physicians in selecting personalized AF treatment and reducing misdiagnosis.