ECG signal detection and classification of heart rhythm diseases based on ResNet and LSTM
Summary: Arrhythmia is one of the most threatening diseases in all kinds of cardiovascular diseases. It is important to achieve efficient and accurate automatic detection of arrhythmias for clinical diagnosis and treatment of cardiovascular diseases. Based on previous research on electrocardiogram (ECG) automatic detection and classification algorithm, this paper uses the ResNet34 network to learn the morphological characteristics of ECG signals and get the significant information of signals, then passes into a three-layer stacked long-term and short-term memory network to get the context dependency of the features. Finally, four classification tasks are implemented on the PhysioNet Challenge 2017 test dataset by using the softmax function. The activation function is changed from the ReLu function to the mish function in this model. Negative information of ECG signals is considered in the training process, which makes the model have more stable and accurate classification ability. In addition, this paper calculates and compares the average information entropy of correctly classified samples and incorrectly classified samples in the test set. Moreover, it eliminates the impact of obvious signal abnormalities (redundancy or loss) on the model classification results, to more comprehensively and accurately explain the classification effect and performance of the model. After eliminating the possibility of abnormal signal, the ResNet34-LSTM3 model obtained an average F1 score of 0.861 and an average area under the receiver operating characteristic curve (ROC) of 0.972 on the test dataset, which indicates that the model can effectively extract the characteristics of ECG signals and diagnose arrhythmia diseases. Comparing the results of the ResNet34 model and ResNet-18 model on the same test dataset, we can see that the improved model in this paper has a better classification and recognition effect on ECG signals as a whole, which can identify atrial fibrillation diseases more effectively.
scientific article; zbMATH DE number 7452845