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An Automatic Tool for Pediatric Heart Sounds Segmentation
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
TCTS Lab,University of Mons, Belgium.
ICT research center, Amir Kabir University, Tehran, Iran.
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

In this paper, we present a novel algorithm for pediatric heart sound segmentation, incorporated into a graphical user interface. The algorithm employs both the Electrocardiogram (ECG) and Phonocardiogram (PCG) signals for an efficient segmentation under pathological circumstances.First, the ECG signal is invoked in order to determine the beginning and end points of each cardiac cycle by using wavelet transform technique. Then, first and second heart sounds within the cycles are identified over the PCG signal by paying attention to the spectral properties of the sounds. The algorithm is applied on 120 recordings of normal and pathological children, totally containing 1976 cardiac cycles. The accuracy of the segmentation algorithm is 97% for S1 and 94% for S2 identification while all the cardiac cycles are correctly determined.

National Category
Biomedical Laboratory Science/Technology Medical Biotechnology
Identifiers
URN: urn:nbn:se:liu:diva-110179OAI: oai:DiVA.org:liu-110179DiVA: diva2:743374
Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2014-09-04Bibliographically 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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Gharehbaghi, ArashSepehri, AmirHult, PeterAsk, Per

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