Abstract:
This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electro...Show MoreMetadata
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.
Published in: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Date of Conference: 03-06 September 2009
Date Added to IEEE Xplore: 13 November 2009
CD:978-1-4244-3296-7
ISSN Information:
PubMed ID: 19963450