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A pattern recognition framework for detecting dynamic changes on cyclic time series
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska fakulteten. Department of Information Science and Media Studies, University of Bergen, Norway.
2015 (engelsk)Inngår i: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, nr 3, s. 696-708Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed  model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.

sted, utgiver, år, opplag, sider
Elsevier, 2015. Vol. 48, nr 3, s. 696-708
Emneord [en]
Hybrid model, cyclic time series, time series, phonocardiogram, systolic murmurs
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-110177DOI: 10.1016/j.patcog.2014.08.017ISI: 000347747000008OAI: oai:DiVA.org:liu-110177DiVA, id: diva2:743369
Tilgjengelig fra: 2014-09-04 Laget: 2014-09-04 Sist oppdatert: 2017-12-05bibliografisk kontrollert
Inngår i avhandling
1. Assessment of Valvular Aortic Stenosis by Signal Analysis of the Phonocardiogram
Åpne denne publikasjonen i ny fane eller vindu >>Assessment of Valvular Aortic Stenosis by Signal Analysis of the Phonocardiogram
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2014. s. 81
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1616
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-110182 (URN)978-91-7519-252-9 (ISBN)
Disputas
2014-09-26, Linden, Campus US, Linköpings universitet, Linköping, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-09-04 Laget: 2014-09-04 Sist oppdatert: 2016-12-28bibliografisk kontrollert

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