Fully automatic electrocardiogram classification system based on generative adversarial network with auxiliary classifier. (15th July 2021) - Zhou, Zhanhong; Zhai, Xiaolong; Tin, Chung
Highlights: Proposed a fully automatic generative adversarial network based system for arrhythmia screening. High F1 score for supraventricular ectopic beat detection against the state-of-arts. Our automatic method performs at comparative level as expert-assisted methods. Adopting discriminator as classifier after fine-tuning boosts final performance. Data augmentation helps relieve class imbalance problem of arrhythmia detection. Abstract: A generative adversarial network (GAN) based fully automatic electrocardiogram (ECG) arrhythmia classification system with high performance is presented in this paper. The generator ( G ) in our GAN is designed to generate various coupling matrix inputs conditioned on different arrhythmia classes for data augmentation. Our designed discriminator ( D ) is trained on both real and generated ECG coupling matrix inputs, and is extracted as an arrhythmia classifier upon completion of training for our GAN. After fine-tuning the 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 10% over the top-performing automatic systems. Moreover, high sensitivity for both SVEB (87%) and VEB (93%) detection has been achieved, which is of great value for practical diagnosis. We, therefore, suggest our ACE-GAN (G enerative A dversarial N etwork with A uxiliary C lassifier for E lectrocardiogram) 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.