Odorant recognition using biological responses recorded in olfactory bulb of rats

Autor: Marcela A. Vizcay, María de la Luz Aylwin, Manuel A. Duarte-Mermoud
Rok vydání: 2015
Předmět:
Zdroj: Computers in Biology and Medicine. 56:192-199
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2014.10.010
Popis: 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. We present an analysis of pattern recognition and dimensionality reduction techniques applied to odor discrimination.The local field potential signals recorded in the olfactory bulb are stimulus specific in normal rats.Artificial olfaction systems discriminate odor information from local field potential recorded in rat olfactory bulb.PCA followed by SVM are able to discriminate odorant stimuli with accuracy of over 95%.
Databáze: OpenAIRE
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