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A novel method for discrimination between innocent and pathological heart murmurs
Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9267-2191
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.
Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
2015 (English)In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 37, no 7, 674-682 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 37, no 7, 674-682 p.
Keyword [en]
Growing-time support vector machine, support vector machine, phonocardiogram signal, heart murmurs, innocent murmurs.
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-117825DOI: 10.1016/j.medengphy.2015.04.013ISI: 000357354400007PubMedID: 26003286OAI: oai:DiVA.org:liu-117825DiVA: diva2:810963
Note

At the time for thesis presentation publication was in status: Manuscript

Available from: 2015-05-08 Created: 2015-05-08 Last updated: 2016-12-28Bibliographically approved
In thesis
1. Assessment of Valvular Aortic Stenosis by Signal Analysis of the Phonocardiogram
Open this publication in new window or tab >>Assessment of Valvular Aortic Stenosis by Signal Analysis of the Phonocardiogram
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Aortic stenosis (AS) is one of the most prevalent valvular heart diseases in elderly people. According to the recommendations of both the American Heart Association and the European Society of Cardiology, severity assessment of AS is primarily based on echocardiographic findings. The experience of the investigator here play important roles in the accuracy of the assessment, and therefore in the disease management. However, access to the expert physicians could be limited, especially in rural health care centers of developing countries.

This thesis aims to develop processing algorithms tailored for phonocardiographic signal with the intension to obtain a noninvasive diagnostic tool for AS assessment and severity grading. The algorithms employ a phonocardiogram as input signal and perform analysis for screening and diagnostics. Such a decision support system, which we call “the intelligent phonocardiography”, can be widely used in primary healthcare centers.

The main contribution of the thesis is to present innovative models for the phonocardiographic analysis by taking the segmental characteristics of the signal into consideration. Three novel methodologies are described, based on the presented models, to perform robust classification. In the first attempt, a novel pattern recognition framework is presented for screening of AS-related murmurs. The framework offers a hybrid model for classifying cyclic time series in general, but is tailored to detect the murmurs as a special case study. The time growing neural network is another method that we use to classify short time signals with abrupt frequency transition. The idea of the growing frames is extended to the cyclic signals with stochastic properties for the screening purposes. Finally, a combined statistical and artificial intelligent classifier is proposed for grading the severity of AS.

The study suggests comprehensive statistical validations not only for the evaluation and representation of systolic murmurs but also for setting the methodology design parameters, which can be considered as one of the significant features of the study. The resulting methodologies can be implemented by using web and mobile technologies to be utilized in distributed healthcare system.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 81 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1616
National Category
Medical Biotechnology Medical Bioscience
Identifiers
urn:nbn:se:liu:diva-110182 (URN)978-91-7519-252-9 (ISBN)
Public defence
2014-09-26, Linden, Campus US, Linköpings universitet, Linköping, 10:00 (English)
Opponent
Supervisors
Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2016-12-28Bibliographically approved

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