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Author Boashash, Boualemen_US
Author Boubchir, Larbien_US
Author Azemi, Ghasemen_US
Available date 2012-03-21T05:46:01Zen_US
Publication Date 2011-12en_US
Publication Name Signal Processing and Information Technology (ISSPIT), 2011 IEEE International Symposium
Citation Boashash, 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. 2011en_US
ISBN 978-1-4673-0752-9en_US
URI http://hdl.handle.net/10576/10808en_US
URI http://dx.doi.org/10.1109/ISSPIT.2011.6151545
Description This 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 http://www.elsevier.com/locate/isbn/0080443354). 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: www.time-frequency.net. 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).en_US
Abstract This 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.en_US
Language enen_US
Publisher IEEEen_US
Subject EEG Classificationen_US
Subject EEG Time-Frequency Analysisen_US
Subject Instantaneous Frequencyen_US
Subject Newborn EEGen_US
Subject Seizureen_US
Subject Time-Frequency Featuresen_US
Subject Time-Frequency Image Processingen_US
Subject time-frequency analysisen_US
Subject time-frequency imagesen_US
Subject time-frequency distributionsen_US
Subject time-frequency detectionen_US
Subject time-frequency classificationen_US
Subject multicomponent EEGen_US
Subject multichannel EEGen_US
Subject Quadratic TFDsen_US
Subject MBDen_US
Subject Modified B distributionen_US
Subject IF model fittingen_US
Subject IF classificationen_US
Subject time-frequency matched filteren_US
Subject EEG abnormalityen_US
Title Time-frequency signal and image processing of non-stationary signals with application to the classification of newborn EEG abnormalitiesen_US
Type Conference Paperen_US


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