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Exploring sustained phonation recorded with acoustic and contact microphones to screen for laryngeal disorders
Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
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2014 (English)In: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Piscataway, NJ: IEEE Press, 2014, 125-132 p.Conference paper (Refereed)
Abstract [en]

Exploration of various features and different structures of data dependent random forests in screening for laryngeal disorders through analysis of sustained phonation recorded by acoustic and contact microphones is the main objective of this study. To obtain a versatile characterization of voice samples, 14 different sets of features were extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We proposed a new, data dependent random forest-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest was also presented. Experimental investigations using a mixed gender database of 273 subjects have shown that the Perceptual linear predictive cepstral coefficients (PLPCC) was the best feature set for both microphones. However, the LP-coefficients and LPCT-coefficients feature sets exhibited good performance in the acoustic microphone case only. Models designed using the acoustic microphone data significantly outperformed the ones built using data recorded by the contact microphone. The contact microphone did not bring any additional information useful for classification. The proposed data dependent random forest significantly outperformed traditional designs. © 2014 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2014. 125-132 p.
Keyword [en]
Random forest, Classification, Laryngeal disorder
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:hh:diva-27447DOI: 10.1109/CICARE.2014.7007844ISI: 000380576000018Scopus ID: 2-s2.0-84922496359ISBN: 978-1-4799-4527-6 (electronic)ISBN: 978-1-4799-4526-9 (electronic)OAI: oai:DiVA.org:hh-27447DiVA: diva2:777719
Conference
CICARE 2014 – 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health, Orlando, Florida, USA, December 9-12, 2014
Note

Funding: grant VP1-3.1-SMM-10-V from the Ministry of Education and Science of Republic of Lithuania

Available from: 2015-01-08 Created: 2015-01-08 Last updated: 2017-03-27Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
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  • Other style
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Output format
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