NDLI: Supervised mutual-information based feature selection for motor unit action potential classification

Content Provider	SpringerLink
Author	Sheikholeslami, N. Stashuk, D.
Copyright Year	1997
Abstract	A new supervised mutual information-based feature selection method is presented. Using real motor unit action potential (MUAP) data from 10 EMG signals, the performances of 32 time-sample feature sets, feature subsets selected using first- and second-order mutual information and features obtained using linear discriminant analysis (LDA) and principal component analysis (PCA) were evaluated using a minimum Euclidean distance (MED) classifier. The evaluation showed that by using only 20 first-order features or only 15 second-order features mean error rates and error rate variations equivalent to using all 32 samples or LDA or PCA could be obtained. The computational cost of first-order feature selection was considerably less than LDA, PCA and second-order feature selection. The performance of first-order features was further evaluated using a more robust classifier. Unlike the MED classifier, the robust classifier only assigned a candidate MUAP if the assignment was sufficiently certain. For the robust classifier the average error rates using 20 features were similar to using the full feature set, yet higher assignment rates were obtained. Results from both evaluations suggest that the sets of first-order features were an efficient representation of lower dimension, which provided high accuracy classification with reduced computational requirements.
Starting Page	661
Ending Page	670
Page Count	10
File Format	PDF
ISSN	01400118
Journal	Medical and Biological Engineering and Computing
Volume Number	35
Issue Number	6
e-ISSN	17410444
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1997-01-01
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Feature selection Mutual information EMG signal decomposition Quantitative EMG Human Physiology Neurosciences Imaging Radiology Computer Applications Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering Computer Science Applications

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