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Communication Dans Un Congrès Année : 2014

Detection of onset in epilepsy signals using generalized Gaussian distribution

Résumé

Extracting information from scalp EEG signals is a challenging biomedical signal processing problem that has a potentially strong impact in the diagnosis and treatment of numerous neurological conditions. In this work we study a new methodology for extracting information from EEG signals from patients suffering from epilepsy. The methodology is based on a multi- resolution wavelet representation and a statistical generalized Gaussian model, which provide a compact description of the time-frequency information in the EEG signal array. Preliminary experiments suggest that the information captured by the model is potentially useful for effectively detecting the onset of epileptic seizures, which is key for epilepsy diagnosis and treatment.

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Dates et versions

hal-03663931 , version 1 (10-05-2022)

Identifiants

  • HAL Id : hal-03663931 , version 1
  • OATAO : 24877

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Antonio Quintero Rincon, Hadj Batatia, Marcelo Pereyra, Marcelo Risk. Detection of onset in epilepsy signals using generalized Gaussian distribution. 5th International Conference on Advances in New Technologies, Interactive Interfaces and Communicability (ADNTIIC 2014), Nov 2014, Córdoba, Argentina. pp.1-8. ⟨hal-03663931⟩
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