Cardiac Murmur Classification Based On An Enhanced Correlation Feature Approach

The heart sound classification aims to explore a reasonable method to distinguish normal heart sounds from abnormal heart sounds.In the traditional heart sound classification,their performance is limited because of the baseline feature of heart sound.This paper investigate the factors that affect the performance of classification tasks and extend the corresponding features.In this work,several basic features and extened features of the heart sound are evaluated based on support vector machine(SVM).In the initial stage,a phonocardiogram(PCG)signal is divided into several segments and 64 features in total should be extracted from time domain and frequency domain,these features are divided into 8 groups according to different property.Feature extension is made for the corresponding results,the frequency domain characteristics of diastole and systole are extended as new features,all these new features are added to the original features and compared in different classification method.In addition,we made a feature selection of wrapper+filter combinations for the enhanced overall features,which is also a process of removing redundancy from a large number of original features and refining features.After optimizing and comparing these features After repeated re-runs of the model,we got a 0.2% improvement in the total score and a large reduction in the dimension of the features,which combined to get a better performance.Finally,for the results of normal and abnormal heart sound signals,the sensitivity,specificity and total score were 95.9%,91.7% and 93.8%,respectively.By comparing with the current mainstream heart sound feature engineering and our proposed algorithm,it can be seen that the algorithm proposed in this article has excellent predictive ability.Based on the use of the final feature model,the machine learning algorithm can have better performance and better performance.Short training period.In the later stage,through the optimization of parameters and the optimization of the algorithm process,its prediction quality and execution efficiency also have a certain potential for improvement.