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Detection of systolic ejection click using time growing neural network
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
University of Mons, Belgium .
Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
Lund University, Sweden .
2014 (English)In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 36, no 4, 477-483 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise.

Place, publisher, year, edition, pages
Elsevier , 2014. Vol. 36, no 4, 477-483 p.
Keyword [en]
Systolic ejection click; Time growing neural network; Time delay neural network; Heart sound
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-106865DOI: 10.1016/j.medengphy.2014.02.011ISI: 000334976800008OAI: diva2:720095
Available from: 2014-05-28 Created: 2014-05-23 Last updated: 2014-09-04
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.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1616
National Category
Medical Biotechnology Medical Bioscience
urn:nbn:se:liu:diva-110182 (URN)978-91-7519-252-9 (print) (ISBN)
Public defence
2014-09-26, Linden, Campus US, Linköpings universitet, Linköping, 10:00 (English)
Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2014-09-04Bibliographically approved

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Gharehbaghi, ArashAsk, Per
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