NDLI: SVM for automatic rodent sleep-wake classification

Content Provider	IEEE Xplore Digital Library
Author	Shantilal Donohue, K.D. O'Hara, B.F.
Copyright Year	2008
Description	Author affiliation: Univ. of Kentucky, Lexington (Shantilal)
Abstract	This work examines the application of a Support Vector Machine (SVM) classifier to automatically detect sleep and wake behaviors in mice based on pressure signals generated by contact with the cage floor. Previous works employed Neural Networks (NN) and Linear Discriminant Analysis (LDA) to classify sleep and wake behaviors. Although the LDA was successful in distinguishing between the sleep and wake behaviors, it has several limitations, which include the need to select a threshold and difficulty separating additional behaviors with subtle differences, such as sleep and rest. The SVM has advantages in that it does not require an independent threshold determination and offers greater degrees of freedom than the LDA for working with complex data sets. In addition, the SVM has direct methods for limiting overfitting for the training sets (unlike the NN method). This paper develops an SVM classifier using a variety of features extracted from the power spectrum, autocorrelation function, and generalized spectrum (autocorrelation of complex spectrum). A genetic algorithm (GA) optimizes the SVM parameters and determines a combination of 5 best features. Experimental results from over 14 hours of data scored by human observation indicate a 95% classification accuracy for SVM.
Starting Page	581
Ending Page	586
File Size	423756
Page Count	6
File Format	PDF
ISBN	9781424418831
DOI	10.1109/SECON.2008.4494360
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Rodents Sleep Linear discriminant analysis Neural networks Autocorrelation Mice Signal generators Feature extraction
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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