基于GAN和MS-ResNet的房颤自动检测模型

doi:10.3969/j.issn.0255-8297.2024.01.002

Abstract

Abstract: Atrial fibrillation (AF) is a common cardiac arrhythmia. However, existing research often relies on single-scale signal segments and overlooks potential complementary information at different scales as well as data imbalance issues, leading to decreased diagnostic performance. This paper proposes a novel AF automatic detection model based on generative adversarial network (GAN) and residual multi-scale network. The model utilizes GAN to synthesize single-lead ECG data with high morphological similarity, hence addressing data privacy and imbalance issues. A multi-scale residual network (MS-ResNet) feature extraction strategy was designed to extract the features of signal segments of different sizes from various scales, so as to effectively capture the features of P wave disappearance and RR interval irregularity. The model combines these two strategies not only to generate high-quality ECG (electrocardiogram) data for the automatic AF detection but also to extract temporal features between different waves using multi-scale grids. The performance of MS-ResNet is evaluated on the PhysioNet Challenge 2017 public ECG dataset and a balanced dataset, comparing it with other existing atrial fibrillation classification models. Experimental results show that the average F1 value and accuracy rate of MS-ResNet on the balanced dataset are 0.914 1 and 91.56%, respectively. Compared with the unbalanced dataset, F1 increases by 4.5%, and the accuracy rate increases by 3.5%.

Key words: electrocardiogram (ECG), atrial fibrillation (AF), generative adversarial network (GAN), multi-scale, automatic detection

CLC Number:

TP391.1

QIN Jing, HAN Yue, WANG Liyong, JI Changqing, LIU Lu, WANG Zumin. An Automatic Atrial Fibrillation Detection Model Based on GAN and MS-ResNet[J]. Journal of Applied Sciences, 2024, 42(1): 15-26.

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An Automatic Atrial Fibrillation Detection Model Based on GAN and MS-ResNet

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