MACHINE-ASSISTED DIAGNOSIS OF CHD BASED ON VARIANT LOGIC THEORY AND MACHINE LEARNING MODELS

Abstract

Cardiac auscultation is a basic means of initial diagnosis of congenital heart disease (CHD). It is significant to analyze Phonocardiogram (PCG) by using signal processing and machine learning techniques for the purpose of machine-assisted diagnosis of CHD. A novel method of machine-assisted diagnosis of CHD was proposed in this paper. First, the duration of the hidden Markov model (DHMM) was applied to locate and segment PCG into each cardiac cycle. Then, the envelope of the PCG was extracted by using Viola integral. After that, the variant logic theory was applied to extract the features and convert the envelope data of the heart sound signal into visual analysis measurement data. Finally, the classifier of the support vector machine (SVM) was used to classify the normal and abnormal heart sounds. There were 1000 cases used in this study. It was divided into a training set of 600 cases, a test set of 200 cases, and validation set of 200 cases. An accuracy of 0.965, a specificity of 0.898, and a sensitivity of 0.937 were achieved using the novel signal processing techniques. The results showed that DHMM and variant logic theory models were suitable for heart sound classification.