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Time-frequency signal and image processing of non-stationary signals with application to the classification of newborn EEG abnormalities

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3 Author(s)
Boualem Boashash ; Electrical Engineering Department, Qatar University College of Engineering, Doha, Qatar ; Larbi Boubchir ; Ghasem Azemi

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.

Published in:

2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Date of Conference:

14-17 Dec. 2011