Stochastic relevance analysis of epileptic EEG signals for channel selection and classification

L. Duque-Munoz, C. Guerrero-Mosquera, G. Castellanos-Dominguez

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

6 Citas (Scopus)

Resumen

Time-frequency decompositions (TFDs) are well known techniques that permit to extract useful information or features from EEG signals, being necessary to distinguish between irrelevant information and the features effectively representing the subjacent physiological phenomena, according to some evaluation measure. This work introduces a new method to obtain relevant features extracted from time-frequency plane for epileptic EEG signals. Particularly, EEG features are extracted by common spectral methods such as short time Fourier transform (STFT), wavelets transform and Empirical Mode Decomposition (EMD). Then, each method is evaluated by Stochastic Relevance Analysis (SRA) that is further used for EEG classification and channel selection. The classification measures are carried out based on the performance of the k-NN classifier, while the channels selected are validated by visual inspection and topographic scalp map. The study uses real and multi-channel EEG data and all the experiments have been supervised by an expert neurologist. Results obtained in this paper show that SRA is a good alternative for automatic seizure detection and also opens the possibility of formulating new criteria to select, classify or analyze abnormal EEG channels.

Idioma originalInglés
Título de la publicación alojada2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Páginas2104-2107
Número de páginas4
DOI
EstadoPublicada - 2013
Publicado de forma externa
Evento2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japón
Duración: 3 jul 20137 jul 2013

Serie de la publicación

NombreProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (versión impresa)1557-170X

Conferencia

Conferencia2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
País/TerritorioJapón
CiudadOsaka
Período3/07/137/07/13

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