Intelligent Diagnosis System of ECG Signal Based on Deep Learning

This study introduces an intelligent diagnosis method based on an improved Transformer, which introduces a multi-scale attention mechanism into the fine feature extraction of the ECG signal, further optimizes the classification model, enhances the loss function, and improves the diagnosis accuracy. This project intends to use the MIT-BIH arrhythmia database as the research object. It divides it into training set, validation set, and test set according to 7:2:1. Experiments show that the accuracy of arrhythmia classification of the method proposed in this paper reaches 98.6%, the recall rate is 98.2%, and the F1 value is 98.4%. Compared with the traditional model, its accuracy is improved by 3.2%, 2.8%, and 3.0%, respectively. Compared with other mainstream deep learning algorithms such as ResNet and Dense Net, the performance indicators of this algorithm have been greatly improved. The research results of this project will provide an efficient and accurate solution for the intelligent diagnosis of ECG signals. It has important scientific significance and practical value.