Design And Implementation Of Heart Sound Classification Algorithm Based On Deep Learning And Ensemble Learning And Intelligent Auscultation System

The prevalence and mortality of cardiovascular diseases are increasing year by year,making it the number one deadly disease in the world today.Heart sound signals can reflect the structure and state of the heart and have important implications for the diagnosis of cardiovascular disease.In the intelligent diagnosis of heart sounds,some products have been introduced,but due to the limitations of algorithm performance,these products can only distinguish whether the heart sounds are normal or not.They need doctors to diagnose the abnormal heart sounds precisely,and there are problems of diagnosis delay and high cost.Based on the above characteristics,this paper presents a heart sound classification algorithm based on deep learning and ensemble learning,and builds an intelligent auscultation system.The specific work is as follows:(1)Heart Sound Network(HSNet)based on convolutional neural network was constructed.Based on the classical convolutional neural network structure AlexNet and VGGNet,this paper constructs a heart sound classification network based on the characteristics of heart sound time domain and frequency domain.In this paper,the public data set is used for experiments,and the classification results in the relevant literature are compared and analyzed.HSNet achieved an accuracy of 97.32% in the normal classification of heart sounds and abnormal heart sounds,0.27% higher than the modified Alex Net,and the HSNet preprocessing process is simple and easy to implement.After verifying the effectiveness of HSNet,this paper train HSNet with self-built datasets,achieving 98.72% accuracy in normal heart sounds,atrial premature beats,ventricular septal defects,and other categories.(2)Heart Sound Net Boosting(HSNBoost)based on deep learning and ensemble learning was proposed.HSNBoost uses the deep learning model HSNet as the feature extractor,and uses the ensemble learning model XGBoost(eXtreme Gradient Boosting)to classify the heart sounds after extracting features.The classification accuracy on the public dataset and self-built dataset is 1.68% and 2.33% higher than HSNet respectively.(3)Intelligent auscultation system was developed.This paper develops the user client and server of the intelligent auscultation system.The functions of user registration and login,user personal information management,heart sound recording and user inquiry are implemented on the user client.The server implements the functions of user information management,HSNBoost prediction and result integration,semi-supervised heart sound label and HSNBoost model update.