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AuthorBoashash, Boualem
AuthorBoubchir, Larbi
AuthorAzemi, Ghasem
Available date2012-03-21T05:46:01Z
Publication Date2011-12
Publication NameSignal Processing and Information Technology (ISSPIT), 2011 IEEE International Symposium
CitationBoashash, B.; Boubchir, L.; Azemi, G., "Time-frequency signal and image processing of non-stationary signals with application to the classification of newborn EEG abnormalities," Signal Processing and Information Technology (ISSPIT), 2011 IEEE International Symposium on , vol., no., pp.120,129, 14-17 Dec. 2011
DescriptionThis paper demonstrates that it is possible to improve the classification of EEG non-stationary signals by using new T-F features based on image processing techniques. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).
AbstractThis paper presents an introduction to time-frequency (T-F) methods in signal processing, and a novel approach for EEG abnormalities detection and classification based on a combination of signal related features and image related features. These features which characterize the non-stationary nature and the multi-component characteristic of EEG signals, are extracted from the T-F representation of the signals. The signal related features are derived from the T-F representation of EEG signals and include the instantaneous frequency, singular value decomposition, and energy based features. The image related features are extracted from the T-F representation considered as an image, using T-F image processing techniques. These combined signal and image features allow to extract more information from a signal. The results obtained on newborn and adult EEG data, show that the image related features improve the performance of the EEG seizure detection in classification systems based on multi-SVM classifier.
SubjectEEG Classification
SubjectEEG Time-Frequency Analysis
SubjectInstantaneous Frequency
SubjectNewborn EEG
SubjectTime-Frequency Features
SubjectTime-Frequency Image Processing
Subjecttime-frequency analysis
Subjecttime-frequency images
Subjecttime-frequency distributions
Subjecttime-frequency detection
Subjecttime-frequency classification
Subjectmulticomponent EEG
Subjectmultichannel EEG
SubjectQuadratic TFDs
SubjectModified B distribution
SubjectIF model fitting
SubjectIF classification
Subjecttime-frequency matched filter
SubjectEEG abnormality
TitleTime-frequency signal and image processing of non-stationary signals with application to the classification of newborn EEG abnormalities
TypeConference Paper

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