NDLI: Discrimination of axonal neuropathy using sensitivity and specificity statistical measures

Content Provider	Springer Nature Link
Author	Procházka, Aleš Vyšata, Oldřich Ťupa, Ondřej Yadollahi, Mohammadreza Vališ, Martin
Copyright Year	2014
Abstract	In needle electromyography, general mathematical methods of pattern recognition and signal analysis can be used to detect motor unit potentials and to classify various types of muscle diseases. The goal of the paper is to contribute to various methods enabling discrimination of individuals with axonal neuropathy (a positive set) from normal cases (a control set) using signals acquired from muscle activities. Data from a control set of 104 individuals and a set of 76 patients were used to validate selected methods of their separation and classification. Different features in both the time and frequency domains were studied to obtain the most reliable results . This novel approach involves comparison of individual features based on adaptive signal thresholding, as well as their combination, using supervised sigmoidal neural networks for their classification. Specificity, sensitivity and accuracy of the features used to detect individuals with axonal neuropathy was analysed using the receiver operating characteristic curves and confusion analysis. An accuracy higher than 93 % was achieved for the given sets of individuals and the optimal criterion values. The proposed modified Willison amplitude together with statistical and spectral properties of signal components classified individuals into sets of healthy and neuropathic patients by artificial neural networks with sensitivity 96.1 % and sufficient accuracy in the wide range of criterion values.
Starting Page	1349
Ending Page	1358
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	25
Issue Number	6
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2014-05-27
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Needle electromyography Axonal neuropathy Signal features detection Selectivity and specificity measures ROC curves Signal classification Digital signal processing Computational intelligence Artificial neural networks Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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