EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures

38Citations
Citations of this article
35Readers
Mendeley users who have this article in their library.

Abstract

In this paper, features which are usually employed in automatic speech recognition (ASR) are used for the detection of seizures in newborn EEG. In particular, spectral envelope-based features, composed of spectral powers and their spectral derivatives are compared to the established feature set which has been previously developed for EEG analysis. The results indicate that the ASR features which model the spectral derivatives, either full-band or localized in frequency, yielded a performance improvement, in comparison to spectral-power-based features. Indeed it is shown here that they perform reasonably well in comparison with the conventional EEG feature set. The contribution of the ASR features was analyzed here using the support vector machines (SVM) recursive feature elimination technique. It is shown that the spectral derivative features consistently appear among the top-rank features. The study shows that the ASR features should be given a high priority when dealing with the description of the EEG signal. © 2011 IEEE.

Cite

CITATION STYLE

APA

Temko, A., Nadeu, C., Marnane, W., Boylan, G., & Lightbody, G. (2011). EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures. IEEE Transactions on Information Technology in Biomedicine, 15(6), 839–847. https://doi.org/10.1109/TITB.2011.2159805

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free