Atrial fibrillation analysis by deep learning.

Abstract

Atrial fibrillation (AF), an increasingly prevalent cardia carrhythmia, is a major contributor to stroke, heart failure, and premature mortality. Traditional manual screening for AF using electrocardiography (ECG) is not only time-consuming but also susceptible to human error, underscoring the urgent need for automated diagnostic tools. This study addresses this challenge by developing advanced computer-aided diagnostic methods leveraging deep learning for the automatic detection of AF. We introduce innovative one-dimensional (1D) and two-dimensional (2D) convolutional neural network (CNN) models specifically designed for the precise classification of ECG signals into normal or atrial fibrillation categories. Our methodology includes a meticulous preprocessing phase where each ECG record is filtere dan dpeak sare accurately detected using the XQRS algorithm. The signals are then segmented into beats with an 80-sample window, which serve as critical features for subsequent classification. The extracted features are fed into our CNN architectures for classification. The models are trained and evaluated using the MIT-BIH Atrial Fibrillation Database, and their generalization capability is further validated with unseen data from the PhysioNet/Computing in Cardiology Challenge 2017 database, following an inter-subject approach. To enhance the robustness of our models, we employ data augmentation techniques. Our comprehensive evaluation demonstrates that the 1D-CNN model achieves a remarkable total accuracy of 95% and an F1 score of 96.81%, while the 2D-CNN model attains an exceptional accuracy and F1 score of 99.84%. These results underscore the efficacy of our approach in accurately classifying ECG signals and highlight the potential of our models for real-world clinical applications, offering a substantial improvement in AF screening processes.