EEG Signal Description with Spectral-Envelope-Based Speech Recognition Features for Detection of Neonatal Seizures

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 whic...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2011-11, Vol.15 (6), p.839-847
Main Authors: Temko, A., Nadeu, C., Marnane, W., Boylan, G. B., Lightbody, G.
Format: Article
Language:English
Subjects:
Algorithms
Automatic speech recognition
Cepstral analysis
Diagnosis, Computer-Assisted - methods
Discrete cosine transforms
EEG
Electroencephalography
Electroencephalography - methods
Feature extraction
Humans
Infant, Newborn
Infant, Newborn, Diseases - diagnosis
Informàtica
Intel·ligència artificial
Llenguatge natural
neonatal seizure detection
Pattern Recognition, Automated - methods
Pediatrics
Reconeixement automàtic de la parla
Seizures - diagnosis
Sensitivity and Specificity
Signal Processing, Computer-Assisted
spectral envelope
spectral slope
Speech Acoustics
Speech Production Measurement
Speech recognition
speech recognition features
Support vector machines
Àrees temàtiques de la UPC
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Summary: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.
ISSN:1089-7771
2168-2194
1558-0032
2168-2208
DOI:10.1109/TITB.2011.2159805