Fully Automatic Electrocardiogram Classification System based on Generative Adversarial Network with Auxiliary Classifier
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A generative adversarial network (GAN) based fully automatic electrocardiogram (ECG) arrhythmia classification system with high performance is presented in this paper. We have designed a discriminator (D) to take ECG coupling matrix as input, and then predict the input validity (real or generated) as well as arrhythmia classes. The generator (G) in our GAN is designed to generate various coupling matrix inputs conditioned on different arrhythmia classes for data augmentation. Upon completion of training for our GAN, we extracted the trained D as an arrhythmia classifier in a transfer learning manner. After fine-tuning D by including patient-specific normal beats estimated using an unsupervised algorithm, and generated abnormal beats by G that are usually rare to obtain, our fully automatic system showed superior overall classification performance for both supraventricular ectopic beats (SVEB or S beats) and ventricular ectopic beats (VEB or V beats) on the MIT-BIH arrhythmia database. It surpassed several state-of-art automatic classifiers and can perform on similar levels as some expert-assisted methods. In particular, the F1 score of SVEB has been improved by up to 12 top-performing automatic systems. Moreover, high sensitivity for both SVEB (85 practical diagnosis. We, therefore, suggest our ACE-GAN (Generative Adversarial Network with Auxiliary Classifier for Electrocardiogram) based automatic system can be a promising and reliable tool for high throughput clinical screening practice, without any need of manual intervene or expert assisted labeling.
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