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A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
2018 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 29, no 9, p. 4102-4115, article id 8066455Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. Vol. 29, no 9, p. 4102-4115, article id 8066455
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-40920DOI: 10.1109/TNNLS.2017.2754294ISI: 000443083700014Scopus ID: 2-s2.0-85052718715OAI: oai:DiVA.org:mdh-40920DiVA, id: diva2:1248078
Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2018-09-13Bibliographically approved

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Ghareh Baghi, ArashLindén, Maria

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CiteExportLink to record
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