| Titre : | Epilepsy classification using machine learning techniques | | Type de document : | projet fin études | | Auteurs : | Sarra BERGUIA, Auteur | | Langues : | Français (fre) | | Catégories : | Ingénierie de web et Informatique mobile
| | Mots-clés : | Machine Learning, Classification, Epilepsy, Electroencephalogram (EEG),
Epileptic seizure, Empirical mode decomposition (EMD), Discret wavelet transform
(DWT). | | Index. décimale : | 2190/19 | | Résumé : | The electroencephalogram (EEG) signal is very important in the diagnosis of
epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG
data. The detection of epileptic activity is, therefore, a very demanding process that
requires a detailed analysis of the entire length of the EEG data, usually performed by an
expert. This paper describes an automated classification of EEG signals for the detection
of epileptic seizures using wavelet transform and empirical mode decomposition (EMD).
The decision making process is comprised of three main stages: (a) feature extraction
based on wavelet transform, (b) feature extraction using empirical mode decomposition,
and (c) classification by support vector machine (SVM), K nearest Neighbors (KNN) and
Artificial neural networks (ANN). The proposed methodology was applied on EEG data
sets that belong to three subject groups: healthy subjects, epileptic subjects during a
seizure-free interval, and epileptic subjects during a seizure. The results confirmed that the
proposed algorithm has a potential in the classification of EEG signals and detection of
epileptic seizures, and could thus further improve the diagnosis of epilepsy.
|
Epilepsy classification using machine learning techniques [projet fin études] / Sarra BERGUIA, Auteur . - [s.d.]. Langues : Français ( fre) | Catégories : | Ingénierie de web et Informatique mobile
| | Mots-clés : | Machine Learning, Classification, Epilepsy, Electroencephalogram (EEG),
Epileptic seizure, Empirical mode decomposition (EMD), Discret wavelet transform
(DWT). | | Index. décimale : | 2190/19 | | Résumé : | The electroencephalogram (EEG) signal is very important in the diagnosis of
epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG
data. The detection of epileptic activity is, therefore, a very demanding process that
requires a detailed analysis of the entire length of the EEG data, usually performed by an
expert. This paper describes an automated classification of EEG signals for the detection
of epileptic seizures using wavelet transform and empirical mode decomposition (EMD).
The decision making process is comprised of three main stages: (a) feature extraction
based on wavelet transform, (b) feature extraction using empirical mode decomposition,
and (c) classification by support vector machine (SVM), K nearest Neighbors (KNN) and
Artificial neural networks (ANN). The proposed methodology was applied on EEG data
sets that belong to three subject groups: healthy subjects, epileptic subjects during a
seizure-free interval, and epileptic subjects during a seizure. The results confirmed that the
proposed algorithm has a potential in the classification of EEG signals and detection of
epileptic seizures, and could thus further improve the diagnosis of epilepsy.
|
|
219/01 
Faire une suggestion Affiner la recherche
Epilepsy classification using machine learning techniques / Sarra BERGUIA
