<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Epilepsy is a chronic neurological disorder that is caused by unprovoked recurrent seizures. The most commonly used tool for the diagnosis of epilepsy is the electroencephalogram (EEG) whereby the electrical activity of the brain is measured. In order to prevent potential risks, the patients have to be monitored as to detect an epileptic episode early on and to provide prevention measures. Many different research studies have used a combination of time and frequency features for the automatic recognition of epileptic seizures. In this paper, two fusion methods are compared. The first is based on an ensemble method and the second uses the Choquet fuzzy integral method. In particular, three different machine learning approaches namely RNN, ML and DNN are used as inputs for the ensemble method and the Choquet fuzzy integral fusion method. Evaluation measures such as confusion matrix, AUC and accuracy are compared as well as MSE and RMSE are provided. The results show that the Choquet fuzzy integral fusion method outperforms the ensemble method as well as other state-of-the-art classification methods.</p>
</abstract>

Epilepsy is a chronic neurological disorder that is caused by unprovoked recurrent seizures. The most commonly used tool for the diagnosis of epilepsy is the electroencephalogram (EEG) whereby the electrical activity of the brain is measured. In order to prevent potential risks, the patients have to be monitored as to detect an epileptic episode early on and to provide prevention measures. Many different research studies have used a combination of time and frequency features for the automatic recognition of epileptic seizures. In this paper, two fusion methods are compared. The first is based on an ensemble method and the second uses the Choquet fuzzy integral method. In particular, three different machine learning approaches namely RNN, ML and DNN are used as inputs for the ensemble method and the Choquet fuzzy integral fusion method. Evaluation measures such as confusion matrix, AUC and accuracy are compared as well as MSE and RMSE are provided. The results show that the Choquet fuzzy integral fusion method outperforms the ensemble method as well as other state-of-the-art classification methods.

Performance Analysis of Data Fusion Methods Applied to Epileptic Seizure Recognition

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Epilepsy is a chronic neurological disorder that is caused by unprovoked recurrent seizures. The most commonly used tool for the diagnosis of epilepsy is the...

Performance Analysis of Data Fusion Methods Applied to Epileptic Seizure Recognition

Publié en ligne: 08 oct. 2021

Pages: 5 - 17

Reçu: 30 juin 2020

Accepté: 24 juin 2021

DOI: https://doi.org/10.2478/jaiscr-2022-0001

Mots clés
epilepsy, ensemble method, Choquet fuzzy integral fusion

© 2022 Simone A. Ludwig, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Performance Analysis of Data Fusion Methods Applied to Epileptic Seizure Recognition

Publié en ligne: 08 oct. 2021

Pages: 5 - 17

Reçu: 30 juin 2020

Accepté: 24 juin 2021

DOI: https://doi.org/10.2478/jaiscr-2022-0001

Mots clésepilepsy, ensemble method, Choquet fuzzy integral fusion

© 2022 Simone A. Ludwig, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
epilepsy, ensemble method, Choquet fuzzy integral fusion