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A pattern recognition framework for detecting dynamic changes on cyclic time series
Linköping University, Sweden.
Linköping University, Sweden.
Linköping University, Sweden.
2015 (engelsk)Inngår i: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, nr 3, s. 696-708Artikkel, forskningsoversikt (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 DEBs 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 the 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
2015. Vol. 48, nr 3, s. 696-708
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-29682DOI: 10.1016/j.patcog.2014.08.017ISI: 000347747000008Scopus ID: 2-s2.0-84916620494OAI: oai:DiVA.org:mdh-29682DiVA, id: diva2:875947
Tilgjengelig fra: 2015-12-02 Laget: 2015-11-26 Sist oppdatert: 2017-12-01bibliografisk kontrollert

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Ghareh Baghi, Arash
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