Categorization of ECG signals based on the dense recurrent network

Electrocardiograph (ECG) signals are an important source of data on human heart health and are widely used to detect different types of arrhythmias. With the development of deep learning, end-to-end ECG classification models based on neural networks have emerged, but most of the existing models are only for small datasets, and there is a problem of poor generalization ability. In order to solve the above two problems, we present a novel deep learning architecture, DRNet, which integrates DenseNet with bidirectional long short-term memory (BiLSTM) to form the Dense (D) Recurrent (R) Network with BiLSTM serving as the recurrent structure. The DenseNet layer is used to extract the deep features of the ECG signal; and the BiLSTM layer can aggregate the extracted features in time, which is more sensitive to the timing status of the ECG. These deep features enrich the temporal background and serve as the foundation for subsequent classification tasks. In the classification stage, the model categorizes ECG signals into one of four types: "normal," "AF" (atrial fibrillation), "other rhythms," and "noisy." In addition, in order to solve the problem of misclassification caused by imbalanced ECG signal samples, an improved cross-entropy loss function, a multiclass concentration loss function, is proposed in this paper, and the partial derivative solution process is given. Finally, in the prediction of ECG signals, the existing simple voting is improved to a weighted voting method. The comparison experiment with the ordinary dense neural network on the published larger ECG dataset proves that this method not only simplifies feature extraction but also greatly enhances the generalization ability of the network.