Identifying neural discharges using time-frequency distributions for EEG

Carlos Guerrero-Mosquera, Angel Navia Vazquez, Armando Malanda Trigueros

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

Resum

This paper presents a time-frequency approach as a nonlinear signal EEG processing technique. The proposed method is based on the use of the Smoothed Pseudo Wigner-Ville distribution (SPWV) good resolution combined with Mc Aulay-Quatieri (MQ) sinusoidal model to identify a neural discharge. The initial results show the algorithm as a suitable method to develop an automatic detector based on graphics patterns parameterized by the features present in the neural discharges on the time-frequency plane. We obtained three features based on energy, frequency and tracking and the algorithm is tested in an application with epileptic EEGs. We can isolate a continuous energy trace with other oscillations when the epileptic seizure is beginning. This characteristic is always present in 16 different seizures from 6 epileptic patients.

Idioma originalAnglès
Títol de la publicacióICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications
Pàgines1563-1566
Nombre de pàgines4
DOIs
Estat de la publicacióPublicada - 2007
Publicat externament
Esdeveniment2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007 - Dubai, United Arab Emirates
Durada: 14 de nov. 200727 de nov. 2007

Sèrie de publicacions

NomICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications

Conferència

Conferència2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007
País/TerritoriUnited Arab Emirates
CiutatDubai
Període14/11/0727/11/07

Fingerprint

Navegar pels temes de recerca de 'Identifying neural discharges using time-frequency distributions for EEG'. Junts formen un fingerprint únic.

Com citar-ho