title: A novel method for discrimination between innocent and pathological heart murmurs.
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.
authors.affiliation: Physiological Measurements, Department of Biomedical Engineering, Linköping University, Linköping, Sweden. Electronic address: arash.ghareh.baghi@liu.se.
authors.forename: Arash
authors.initials: A
authors.lastname: Gharehbaghi
authors.affiliation: Physiological Measurements, Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
authors.forename: Magnus
authors.initials: M
authors.lastname: Borga
authors.affiliation: Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden; School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
authors.forename: Birgitta Janerot
authors.initials: BJ
authors.lastname: Sjöberg
authors.affiliation: Physiological Measurements, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
authors.forename: Per
authors.initials: P
authors.lastname: Ask
mesh_terms: D000293:Adolescent; D000368:Aged; D000369:Aged, 80 and over; D001024:Aortic Valve Stenosis / Q000145:classification / Q000503:physiopathology; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D006337:Heart Murmurs / Q000145:classification* / Q000503:physiopathology; D006801:Humans; D007223:Infant; D008875:Middle Aged; D010701:Phonocardiography / Q000379:methods*; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D060388:Support Vector Machine; D058067:Wavelet Analysis
keywords: Growing-time support vector machine; Heart murmurs; Innocent murmurs; Phonocardiogram; Support vector machine