Improving the classification of newborn EEG time-frequency representations using a combined time-frequency signal and image approach

Boashash, Boualem, Boubchir, Larbi and Azemi, Ghasem (2012). Improving the classification of newborn EEG time-frequency representations using a combined time-frequency signal and image approach. In: 2012 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA 2012). Proceedings. ISSPA 2012: The 11th International Conference on Information Science, Signal Processing and their Applications, Montreal, QC, Canada, (280-285). 2-5 July, 2012. doi:10.1109/ISSPA.2012.6310560


Author Boashash, Boualem
Boubchir, Larbi
Azemi, Ghasem
Title of paper Improving the classification of newborn EEG time-frequency representations using a combined time-frequency signal and image approach
Conference name ISSPA 2012: The 11th International Conference on Information Science, Signal Processing and their Applications
Conference location Montreal, QC, Canada
Conference dates 2-5 July, 2012
Proceedings title 2012 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA 2012). Proceedings
Journal name 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012
Place of Publication Piscataway, NJ, USA
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publication Year 2012
Sub-type Fully published paper
DOI 10.1109/ISSPA.2012.6310560
ISBN 9781467303811
9781467303828
9781467303804
Start page 280
End page 285
Total pages 6
Language eng
Formatted Abstract/Summary
This paper presents new time-frequency (T-F) features to improve the classification of non-stationary signals such as EEG signals. Previous methods were based only on signal features that were derived from the instantaneous frequency and energies of EEG signals in different spectral sub-bands. This paper includes new features that are based on T-F image descriptors which are extracted from the T-F representation considered as an image, using T-F image processing techniques. The results obtained on newborn EEG data, show that the use of image related-features with signal based-features improve the performance of the newborn EEG seizure detection and classification when using multi-SVM classifiers. These results allow the possibility of improving health outcomes for sick babies by early intervention on the basis of the results of the classification of newborn EEG abnormalities.
Subjects 1706 Computer Science Applications
1711 Signal Processing
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Collection: UQ Centre for Clinical Research Publications
 
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