Classification of Imbalanced Electrocardiosignal Data using Convolutional Neural Network

In the application of wearable heart-monitors, it is of great significance to analyze electrocardiogram (ECG) signals for anomaly detection. ECG arrhythmia classification remains an open problem in that it cannot easily recognize data from minority classes due to the imbalanced dataset and particular characteristic of the time series signal. In this study, a novel method is presented as a possible solution to imbalanced classification problems.

An improved data augmentation method based on variational auto-encoder (VAE) and auxiliary classifier generative adversarial network (ACGAN) is implemented to address the difficulties resulting from the imbalanced dataset. Based on the augmented dataset, convolutional neural network (CNN) classifiers are employed to automatically recognize arrhythmias using two-dimensional ECG images.

In experimental studies conducted with the MIT-BIH arrhythmia database, the proposed method achieves 98.45% accuracy and 97.03% sensitivity. The sensitivities of two minority classes achieve 95.83% and 97.37%, respectively.

In imbalanced classification, the sensitivity of minority class is a key evaluation indicator. One of the significant contributions of this study is that the proposed method can obtain higher sensitivity of minority class. The experimental results demonstrate that the proposed method for ECG arrhythmia calssification under imbalanced data has better performance compared with traditional cropping augmentation methods and traditional classifiers.