NDLI: Feature Extraction for Systolic Heart Murmur Classification

Content Provider	Springer Nature Link
Author	Ahlstrom, Christer Hult, Peter Rask, Peter Karlsson, Jan Erik Nylander, Eva Dahlström, Ulf Ask, Per
Copyright Year	2006
Abstract	Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.
Starting Page	1666
Ending Page	1677
Page Count	12
File Format	PDF
ISSN	00906964
Journal	Annals of Biomedical Engineering
Volume Number	34
Issue Number	11
e-ISSN	15739686
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2006-10-04
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Auscultation Bioacoustics Feature selection Heart sounds Valvular disease Biochemistry Mechanics Biophysics/Biomedical Physics Biomedical Engineering Biomedicine general
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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Feature Extraction From Parametric Time–Frequency Representations for Heart Murmur Detection

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