| Συγγραφείς: | Maglogiannis H., Zafiropoulos E., Loukis E., Stasis A. |
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| Τίτλος: | Support Vectors Machine based Identification of Heart Valve Diseases Using Heart Sounds |
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| Περιοδικό: | Computer Methods and Programs in Biomedicine (Science Citation Index, SCOPUS, Elsevier) |
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| Volume: | 95 |
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| Αριθμός: | 1 |
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| Σελίδες: | 47-61 |
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| Έτος: | 2009 |
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| Εκδότης: | |
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| Να εμφανιστεί: | Όχι |
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| Δεσμός: | |
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| ISI: | Ναι |
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| Impact Factor: | |
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| Όνομα αρχείου: | Γ25_SVM_Heart_Valve_Disease_2009.pdf##^^&&924942099.pdf |
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| Περίληψη: | Taking into account that heart auscultation remains the dominant method for heart examination
in the small health centers of the rural areas and generally in primary healthcare
set-ups, the enhancement of this technique would aid significantly in the diagnosis of
heart diseases. In this context, the present paper initially surveys the research that has
been conducted concerning the exploitation of heart sound signals for automated and
semi-automated detection of pathological heart conditions. Then it proposes an automated
diagnosis system for the identification of heart valve diseases based on the Support Vector
Machines (SVM) classification of heart sounds. This system performs a highly difficult diagnostic
task (even for experienced physicians), much more difficult than the basic diagnosis
of the existence or not of a heart valve disease (i.e. the classification of a heart sound as
‘healthy’ or ‘having a heart valve disease’): it identifies the particular heart valve disease.
The system was applied in a representative global dataset of 198 heart sound signals, which
come both from healthy medical cases and from cases suffering from the four most usual
heart valve diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis (MS) and
mitral regurgitation (MR). Initially the heart sounds were successfully categorized using a
SVM classifier as normal or disease-related and then the corresponding murmurs in the
unhealthy cases were classified as systolic or diastolic. For the heart sounds diagnosed as
having systolic murmur we used a SVM classifier for performing a more detailed classification
of them as having aortic stenosis or mitral regurgitation. Similarly for the heart sounds
diagnosed as having diastolicmurmur we used a SVM classifier for classifying them as having
aortic regurgitation or mitral stenosis. Alternative classifiers have been applied to the
same data for comparison (i.e. back-propagation neural networks, k-nearest-neighbour and
naïve Bayes classifiers), however their performance for the same diagnostic problems was
lower than the SVM classifiers proposed in this work. |
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English