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

Authors:
Boualem Boashash;Larbi Boubchir;Ghasem Azemi
Affiliations:
Electrical Engineering Department, Qatar University College of Engineering, Doha, Qatar;Electrical Engineering Department, Qatar University College of Engineering, Doha, Qatar;University of Queensland, Centre for Clinical Research and Perinatal Research Centre, Royal Brisbane & Womens Hospital, Herston, 4029, Australia
Venue:
ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
Year:
2011

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On the selection of time-frequency features for improving the detection and classification of newborn EEG seizure signals and other abnormalities

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV

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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.