Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals

Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals

This work aimed to use machine learning to diagnose four heart valve disease conditions and normal heart sounds. This paper proposed the automated classification of normal, aortic stenosis, mitral valve prolapse, mitral stenosis, and mitral regurgitation using phonocardiogram (PCG) signals.

This work proposed a novel graph-based feature generator developed using a graph-based technique called Petersen graph pattern (PGP). In addition, a new decomposition model was proposed using variable-sized overlapping blocks, namely tent pooling (TEP) decomposition. By combining TEP and PGP, a novel multilevel feature generation network was developed. Iterative neighborhood component analysis (INCA) was used to select the features. The selected features were fed to decision tree (DT), linear discriminant (LD), bagged tree (BT), and support vector machine (SVM) classifiers for automated classification into five classes.

The proposed method's results yielded 100.0% classification accuracy using the k nearest neighbor (kNN) classifier with a ten-fold cross-validation strategy in classifying the five classes. DT, LD, BT, SVM classifiers yielded an accuracy of 95.10%, 98.30%, 98.60%, and 99.90%, respectively.

Our attained high classification accuracy suggests that the proposed PGP and TEP based model can be used for heart sound classification using PCG signals.