Identifying neural discharges using time-frequency distributions for EEG

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

Research output: Book chapterConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications
Pages1563-1566
Number of pages4
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007 - Dubai, United Arab Emirates
Duration: 14 Nov 200727 Nov 2007

Publication series

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

Conference

Conference2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007
Country/TerritoryUnited Arab Emirates
CityDubai
Period14/11/0727/11/07

Keywords

  • Detection
  • Epilepsy
  • McAulay-Quatieri sinusoidal analysis
  • Sinwave analysis
  • Time-frequency distributions

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