1. bookVolume 12 (2022): Issue 1 (January 2022)
Journal Details
License
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

Performance Analysis of Data Fusion Methods Applied to Epileptic Seizure Recognition

Published Online: 08 Oct 2021
Page range: 5 - 17
Received: 30 Jun 2020
Accepted: 24 Jun 2021
Journal Details
License
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
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.

Keywords

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