A new approach to sample entropy of multi-channel signals: application to EEG signals

TitreA new approach to sample entropy of multi-channel signals: application to EEG signals
Type de publicationCommunication
TypeCommunication avec actes dans un congrès
Date du colloque03-07/09/2018
Titre du colloqueEUSIPCO 2018. 26th European Signal Processing Conference
Titre des actes ou de la revue2018 26th European Signal Processing Conference (EUSIPCO)
AuteurEl Sayed Hussein Jomaa, Mohamad , Van Bogaert, Patrick , Jrad, Nisrine
1, 2
, Colominas, Marcelo A , Humeau-Heurtier, Anne
Résumé en anglais

In this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the existing method, the one proposed here has the advantage of maintaining good results as the number of channels increases. The new and already-existing algorithms were applied on multivariate white Gaussian noise signals, pink noise signals, and mixtures of both. For high number of channels, the existing method failed to show that white noise is always the most irregular whereas the proposed method always had the entropy of white noise the highest. Application of both algorithms on MIX process signals also confirmed the ability of the proposed method to handle larger number of channels without risking erroneous results. We also applied the proposed algorithm on EEG data from epileptic patients before and after treatments. The results showed an increase in entropy values after treatment in the regions where the focus was localized. This goes in the same way as the medical point of view that indicated a better health state for these patients.

URL de la noticehttp://okina.univ-angers.fr/publications/ua17004
Lien vers le document en ligne