Heart Sound Feature Parameters Distribution and Support Vector Machine-Based Classification Boundary Determination Method for Ventricular Septal Defect Auscultation

Abstract

This paper is concerned with a novel proposal to determinate classification boundaries both in time and frequency domains based on the support vector machines (SVM) technique for diagnosis of ventricular septal defect (VSD). Firstly, two heart sound characteristic waveforms are extracted both from time-domain and frequency-domain. Four heart sound feature parameters both in time and frequency domains, [ T 11 , T 12 ] and [ F G , F W ], are obtained from the crossed points of the waveforms at the selected threshold values. Secondly, a novel algorithm to determine the classification boundaries surrounding the feature parameters is proposed with the aid of SVM technique for evaluating the performance of VSD auscultation. Finally, the classification labeling indexes based on the classification boundaries are introduced for diagnosis of VSD. A case study on the normal and VSD cases is demonstrated to validate the usefulness and e ffi ciency of the proposed method; the classification accuracy (CA) is gained 98.4% for diagnosing VSD from the normal cases. Furthermore, the proposed method is applied to classify the sizes of the defect in VSD. The accuracies have been achieved at 94 . 9% for small, 93 . 6% for moderate and 95 . 8% for large VSD. heart sound samples, and the results showed that the accuracies are 98 . 4% for classification of VSD from normal sounds, 94 . 9% for SVSD from other VSD sounds, 93 . 6% for MVSD and 95 . 8% for LVSD, respectively. Furthermore, the methodology proposed in this paper shows a high potential to be extended to the other heart disorder diagnosis.