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New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

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Abstract

This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time–frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.

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Acknowledgments

This work has been funded by the Spain CICYT grant TEC2008-02473.

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Authors and Affiliations

  1. Signal Processing and Communications Department, University Carlos III of Madrid, Madrid, Spain

    Carlos Guerrero-Mosquera, Armando Malanda Trigueros, Jorge Iriarte Franco & Ángel Navia-Vázquez

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  1. Carlos Guerrero-Mosquera
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  2. Armando Malanda Trigueros
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  3. Jorge Iriarte Franco
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  4. Ángel Navia-Vázquez
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Correspondence to Carlos Guerrero-Mosquera.

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Guerrero-Mosquera, C., Malanda Trigueros, A., Iriarte Franco, J. et al. New feature extraction approach for epileptic EEG signal detection using time-frequency distributions. Med Biol Eng Comput 48, 321–330 (2010). https://doi.org/10.1007/s11517-010-0590-5

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