Publication: New approach in features extraction for EEG signal detection
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ISSN: 1557-170X
ISBN: 978-1-4244-3296-7
Publication date
2009-11
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Tutors
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Publisher
IEEE
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Abstract
This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the Smoothed Pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature based on the length of the track that, combined with energy and frequency features, allows to isolate a continuous energy trace from another oscillations when an epileptic seizure is beginning. We evaluate our approach using data consisting of 16 different seizures from 6 epileptic patients. The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs.
Description
4 pages, 3 figures.-- Contributed to: "Engineering the Future of Biomedicine", EMBC2009, 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Minneapolis, Minnesota, USA, Sep 2-6, 2009).
Keywords
EEG signal detection, McAulay-Quatieri sinusoidal model, Abnormal neural discharges, Electroencephalogram, Epileptic seizures, Feature extraction, Smoothed pseudo Wigner-Ville distribution, Time-frequency distributions
Bibliographic citation
Proceedings 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, p. 13-16