DCNN: Dual-Level Collaborative Neural Network for Imbalanced Heart Anomaly Detection

The electrocardiogram (ECG) plays an important role in assisting clinical diagnosis such as arrhythmia detection. However, traditional techniques for ECG analysis are time-consuming and laborious. Recently, deep neural networks have become a popular technique for automatically tracking ECG signals, which has demonstrated that they are more competitive than human experts. However, the minority class of life-threatening arrhythmias causes the model training to skew towards the majority class. To address the problem, we propose a dual-level collaborative neural network (DCNN), which includes data-level and cost-sensitive level modules. In the Data Level module, we utilize the generative adversarial network with Unet as the generator to synthesize ECG signals. Next, the Cost-sensitive Level module employs focal loss to increase the cost of incorrect prediction of the minority class. Empirical results show that the Data Level module generates highly accurate ECG signals with fewer parameters. Furthermore, DCNN has been shown to significantly improve the classification of the ECG.