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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 forEEGanalysis.The results indicate that the ASR featureswhich 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.
CitationTemko, A. [et al.]. EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures. "IEEE transactions on information technology in biomedicine", 21 Novembre 2011, vol. 15, núm. 6, p. 839-847.
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