RT Conference Proceedings
T1 New approach in features extraction for EEG signal detection
A1 Guerrero Mosquera, Carlos Andrés
A1 Navia Vázquez, Ángel
AB 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.
PB IEEE
SN 978-1-4244-3296-7
SN 1557-170X
YR 2009
FD 2009-11
LK https://hdl.handle.net/10016/9098
UL https://hdl.handle.net/10016/9098
LA eng
NO 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).
NO This work has been funded by the Spain CICYT grant TEC2008-02473.
DS e-Archivo
RD 1 jul. 2024