By Topic

Feature-based detection of the K-complex wave in the human electroencephalogram using neural networks

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 $13
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

4 Author(s)
Bankman, I.N. ; Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA ; Sigillito, V.G. ; Wise, R.A. ; Smith, P.L.

The main difficulties in reliable automated detection of the K-complex wave in EEG are its close similarity to other waves and the lack of specific characterization criteria. The authors present a feature-based detection approach using neural networks that provides good agreement with visual K-complex recognition: a sensitivity of 90% is obtained with about 8% false positives. The respective contribution of the features and that of the neural network is demonstrated by comparing the results to those obtained with (i) raw EEG data presented to neural networks, and (ii) features presented to Fisher's linear discriminant.

Published in:

Biomedical Engineering, IEEE Transactions on  (Volume:39 ,  Issue: 12 )