Transformer-based Neural Network for Electrocardiogram Classification
Mohammed A. Atiea, Mark Adel
Abstract
A transformer neural network is a powerful method that is used for sequence modeling and classification. In this paper, the transformer neural network was combined with a convolutional neural network (CNN) that is used for feature embedding to provide the transformer inputs. The proposed model accepts the raw electrocardiogram (ECG) signals side by side with extracted morphological ECG features to boost the classification performance. The raw ECG signal and the morphological features of the ECG signal experience two independent paths with the same model architecture where the output of each transformer decoder is concatenated to go through the final linear classifier to give the predicted class. The experiments and results on the PTB-XL dataset with 7-fold cross-validation have shown that the proposed model achieves high accuracy and F-score, with an average of 99.86% and 99.85% respectively, which shows and proves the robustness of the model and its feasibility to be applied in industrial applications.