An Automatic Atrial Fibrillation Detection Model Based on GAN and MS-ResNet
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 diag-nostic 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 address-ing 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 ex-tract 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％.
万方数据
维普
