Computational methods for physiological signal data of heart failure patients and their predictive model design

ECG and PCG reflect the activity characteristics of the heart, and the combination of the two can record the electromechanical activity information of the heart more comprehensively.In this paper, we design a heart failure prediction model based on Transformer, and utilize Transformer Encoder to complete the feature fusion of ECG and PCG.Feature classification is performed using ResNet-18 to achieve the prediction of nine typical arrhythmias.Evaluate the classification results on the dataset to explore the performance level of the proposed model.Obtain ECG and PCG data in real situations, and select entropy analysis and heart rate variability metrics to quantify the physiological signal time series complexity.The model classification accuracy, specificity and sensitivity are compared to analyze the effect and superiority of the proposed model in practical applications.The results show that the average accuracy of the model on the four datasets reaches 92.28%, and the highest average F1 score is 0.930.In practical applications, the classification accuracy, specificity and sensitivity of the proposed model in this paper are 96.79%,97.47% and 96.77%, respectively.Through the fusion analysis of ECG signal and heart sound signal characteristics, the model fully reflects the HRV change characteristics of heart failure patients and can effectively predict heart failure.