Guerrero Mosquera, Carlos AndrésNavia Vázquez, Ángel2010-07-192010-07-192009-11Proceedings 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, p. 13-16978-1-4244-3296-71557-170Xhttps://hdl.handle.net/10016/90984 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).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.application/pdfeng© IEEE© Engineering in Medicine and Biology Society (EMBS)EEG signal detectionMcAulay-Quatieri sinusoidal modelAbnormal neural dischargesElectroencephalogramEpileptic seizuresFeature extractionSmoothed pseudo Wigner-Ville distributionTime-frequency distributionsconference paperTelecomunicaciones10.1109/IEMBS.2009.5332434open access