Document Detail


Odorant recognition using biological responses recorded in olfactory bulb of rats.
MedLine Citation:
PMID:  25464359     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
In this study we applied pattern recognition (PR) techniques to extract odorant information from local field potential (LFP) signals recorded in the olfactory bulb (OB) of rats subjected to different odorant stimuli. We claim that LFP signals registered on the OB, the first stage of olfactory processing, are stimulus specific in animals with normal sensory experience, and that these patterns correspond to the neural substrate likely required for perceptual discrimination. Thus, these signals can be used as input to an artificial odorant classification system with great success. In this paper we have designed and compared the performance of several configurations of artificial olfaction systems (AOS) based on the combination of four feature extraction (FE) methods (Principal Component Analysis (PCA), Fisher Transformation (FT), Sammon NonLinear Map (NLM) and Wavelet Transform (WT)), and three PR techniques (Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP) and Support Vector Machine (SVM)), when four different stimuli are presented to rats. The best results were reached when PCA extraction followed by SVM as classifier were used, obtaining a classification accuracy of over 95% for all four stimuli.
Authors:
Marcela A Vizcay; Manuel A Duarte-Mermoud; María de la Luz Aylwin
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-11-18
Journal Detail:
Title:  Computers in biology and medicine     Volume:  56C     ISSN:  1879-0534     ISO Abbreviation:  Comput. Biol. Med.     Publication Date:  2014 Nov 
Date Detail:
Created Date:  2014-12-2     Completed Date:  -     Revised Date:  2014-12-3    
Medline Journal Info:
Nlm Unique ID:  1250250     Medline TA:  Comput Biol Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  192-199     Citation Subset:  -    
Copyright Information:
Copyright © 2014 Elsevier Ltd. All rights reserved.
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