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

Authors:
Boualem Boashash;Larbi Boubchir
Affiliations:
Electrical Engineering Department, Qatar University College of Engineering, Doha, Qatar,Centre for Clinical Research, Royal Brisbane & Womens Hospital, University of Queensland, Herston, QLD, ...;Electrical Engineering Department, Qatar University College of Engineering, Doha, Qatar
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Year:
2012

Citing 3
Cited 0

Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
EEG signal classification using wavelet feature extraction and a mixture of expert model

Expert Systems with Applications: An International Journal
Time-frequency signal and image processing of non-stationary signals with application to the classification of newborn EEG abnormalities

ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents new time-frequency features for seizure detection in newborn EEG signals. These features are obtained by translating some relevant time features or frequency features to the joint time-frequency domain. A calibration procedure is then used for verification. The relevant translated features are ranked and selected according to maximal-relevance and minimal-redundancy criteria. The selected features improve the performance of newborn EEG seizure detection and classification systems by up to 4% for 100 real newborn EEG segments.