A novel method for discrimination between innocent and pathological heart murmurs. Issue 7 (July 2015)

• Record Type: Journal Article
• Authors: Gharehbaghi, Arash; Borga, Magnus; Sjöberg, Birgitta Janerot; Ask, Per
• Is Part Of: Medical engineering & physics. Volume 37:Issue 7(2015:Jul.)
• Page Start: 674
• Page End: 682
• Publication Date: 2015-07
• Subjects: Growing-time support vector machine -- Support vector machine -- Phonocardiogram -- Heart murmurs -- Innocent murmurs; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2015.04.013
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: A robust method for discrimination between innocent and pathological murmur is presented. The method is based on statistical classification techniques. The proposed method gives the flexibility to the classifier to be trained for a demanding medical purpose, where very similar patterns are supposed to be distinguished, i.e. mild aortic stenosis and innocent murmur. The study presents a statistical validation method to evaluate the performance of the methods which is another unique feature of the presented study within this context. Results showed an improved classification and also the potential for further improvements in favor of the proposed method. Abstract: This paper presents a novel method for discrimination between innocent and pathological murmurs using the growing time support vector machine (GTSVM). The proposed method is tailored for characterizing innocent murmurs (IM) by putting more emphasis on the early parts of the signal as IMs are often heard in early systolic phase. Individuals with mild to severe aortic stenosis (AS) and IM are the two groups subjected to analysis, taking the normal individuals with no murmur (NM) as the control group. The AS is selected due to the similarity of its murmur to IM, particularly in mild cases. To investigate the effect of the growing time windows, the performance of the GTSVM is compared to that of a conventional support vector machine (SVM), using repeated random sub-sampling method. The mean value of the classification rate/sensitivity is found to be 88%/86% for the GTSVM and 84%/83% for the SVM. The statistical evaluations show that the GTSVM significantly improves performance of the classification as compared to the SVM.