International Conference on Computer and Knowledge Engineering

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Enhanced Atrial Fibrillation (AF) Detection via Data Augmentation with Diffusion Model

Authors :

Arash Vashagh¹ Amirhossein Akhoondkazemi² Sayed Jalal Zahabi³ Davood Shafie⁴

1- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 841568311, Iran 2- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 841568311, Iran 3- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 841568311, Iran 4- Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran

Keywords :

Atrial Fibrillation،ECG،Poincare Recurrence Plot،Data Augmentation،Generative Adversarial Network،GAN،Diffusion Model،Denoising Diffusion Probabilistic Models،DDPM،Deep Learning

Abstract :

Atrial Fibrillation (AF) is the most common type of cardiac arrhythmia, posing significant health risks such as strokes and cardiac failure. Deep learning methods have been employed to automatically diagnose AF from electrocardiogram (ECG) signals. In this study, transforming the ECG signal into two-dimensional Poincaré plots, we explore the application of diffusion model in the augmentation of AF samples to improve the performance of AF detection via deep learning. In this regard, we compare the performance of diffusion model and generative adversarial network (GAN). Our results suggest that diffusion model can potentially generate more satisfactory samples in terms of both the FID score and the downstream classification task.