By Topic

Artificial neural network and spectrum analysis methods for detecting brain diseases from the CNV response in the electroencephalogram

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

8 Author(s)
Jervis, B.W. ; Sch. of Eng. Technol., Sheffield Hallam Univ., UK ; Saatchi, M.R. ; Lacey, A. ; Roberts, T.
more authors

Two methods of identifying schizophrenia, Parkinson's disease (PD), and Huntington's disease (HD) are described. The methods are based on the analysis of the contingent negative variation (CNV), an event related potential (ERP) in the electroencephalogram. The first method involves spectrum analysis of the CNV and discriminant analysis of the Fourier harmonic frequency components. The other method involves the application of supervised learning artificial neural networks to the CNV features obtained in the time domain. Additionally, unsupervised artificial neural networks were used to presymptomatically assess the risk of HD. Sensitivities and specificities lie between 0.81 and 1.0 with low false positive rates (0 to 0.13) for differentiating between disease and normal data, and between disease data, dependent on disease and method. The preferred method for disease differentiation for accuracy and ease of application is the multilayer perceptron. Using Kohonen and ART networks for detecting abnormal CNVs in subjects at risk of HD (ARs) eight abnormals are identified in agreement with the prediction of risk derived from a published risk table. In addition, one of the abnormals has since developed symptomatic Huntington's disease. The recommended method is to combine the results of the Kohonen method with an ART2 and a modified ART1 network

Published in:

Science, Measurement and Technology, IEE Proceedings -  (Volume:141 ,  Issue: 6 )