No abstract

This paper aims at proposing an abnormality detection framework for electrocardiogram (ECG) signals, which owns unbalance distribution among different classes and gaining high accuracy in rhythm/morphology abnormalities classification. The proposed framework is composed of two models: data augmentation model and classification model. In this framework, data augmentation model is designed to recast a class-balanced training dataset by generating artificial data of minor class. The outputs of augmentation model are transferred into classification model. The classification model is designed to identify abnormalities accurately after training using both the experimental and generated datasets. Data augmentation model is supported by auxiliary classifier generative adversarial network (ACGAN). We construct Generator and Discriminator of the ACGAN by stacking multiple 1-dimensional convolutional layers with small kernel size. Dropout function and batch normalization are added to prevent gradients vanish and speed up convergence. In order to evaluate the performance of augmentation model, a set of quantitative indicators are introduced to verify the quality of generated ECG signals. We establish classification model based on stacked residual network parallel connected with long short-term memory (LSTM) network. The experimental study is conducted for single heartbeat detection and consecutive heartbeat detection. The results based on standard benchmark, MIT-BIH, and competition database provided by 2018 China physiological signal challenge (CPSC) have verified the proposed framework can achieve high performance in robustness and accuracy for class-imbalanced dataset.

No abstract

No abstract

No abstract