View source for Publications:Data dependent random forest applied to screening for laryngeal disorders through analysis of sustained phonation : Acoustic versus contact microphone

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{{PublicationSetupTemplate|Author=Antanas Verikas, Adas Gelzinis, Evaldas Vaiciukynas, Marija Bacauskiene, Jonas Minelga, Magnus Hållander, Virgilijus Uloza, Evaldas Padervinskis
|PID=803439
|Name=Verikas, Antanas [av] [0000-0003-2185-8973] (Högskolan i Halmstad [2804], Akademin för informationsteknologi [16904], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941]) (Kaunas University of Technology, Kaunas, Lithuania);Gelzinis, Adas (Kaunas University of Technology, Kaunas, Lithuania);Vaiciukynas, Evaldas (Kaunas University of Technology, Kaunas, Lithuania);Bacauskiene, Marija (Kaunas University of Technology, Kaunas, Lithuania);Minelga, Jonas (Kaunas University of Technology, Kaunas, Lithuania);Hållander, Magnus [cmn] (Högskolan i Halmstad [2804], Akademin för informationsteknologi [16904], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], CAISR Centrum för tillämpade intelligenta system (IS-lab) [13650]);Uloza, Virgilijus (Lithuanian University of Health Sciences, Kaunas, Lithuania);Padervinskis, Evaldas (Lithuanian University of Health Sciences, Kaunas, Lithuania)
|Title=Data dependent random forest applied to screening for laryngeal disorders through analysis of sustained phonation : Acoustic versus contact microphone
|PublicationType=Journal Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=Medical Engineering and Physics
|JournalISSN=1350-4533
|Status=published
|Volume=37
|Issue=2
|HostPublication=
|Conference=
|StartPage=210
|EndPage=218
|Year=2015
|Edition=
|Pages=
|City=London
|Publisher=Elsevier
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|DOI=http://dx.doi.org/10.1016/j.medengphy.2014.12.005
|ISI=000349877200008
|PMID=25618220
|ScopusId=
|NBN=urn:nbn:se:hh:diva-28085
|LocalId=
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|Keywords=Laryngeal disorder;Sustained phonation;Voice;Random forest;Committee;Decision confidence
|Categories=Medicinteknik (206)
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|Notes=<p>This study was supported by a grant VP1-3.1-SMM-10-V from the Ministry of Education and Science of Republic of Lithuania.</p>
|Abstract=<p>Comprehensive evaluation of results obtained using acoustic and contact microphones in screening for laryngeal disorders through analysis of sustained phonation is the main objective of this study. Aiming to obtain a versatile characterization of voice samples recorded using microphones of both types, 14 different sets of features are extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We propose a new, data dependent random forests-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest is 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 linear predictive coefficients (LPC) and linear predictive cosine transform coefficients (LPCTC) 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 the classification. The proposed data dependent random forest significantly outperformed the traditional random forest. (C) 2015 IPEM. Published by Elsevier Ltd. All rights reserved.</p>
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|CreatedDate=2015-04-13
|PublicationDate=2015-04-13
|LastUpdated=2015-04-13
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:803439}}
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