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

<p>A 1-dimensional (1D) and 2-dimensional (2D) biomedical signal analysis based on the Discrete Cosine Transform (DCT) feature extraction method was performed to diagnose epilepsy disorders with high accuracy. For this purpose, Electroencephalogram (EEG) data were used for 1D signal analysis and Magnetic Resonance Imaging (MRI) data were used for 2D signal analysis. The feature vectors were obtained by applying 1D DCT together with statistical methods such as mean, variance, standard deviation, kurtosis, and skewness for EEG data and by applying 2D DCT together with the statistical method of mean for MRI data. The most useful features were selected by applying Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Forward Selection and Backward Selection methods to the obtained feature vectors. Using EEG stand-alone features, MRI stand-alone features and EEG-MRI fused features, the classification of healthy and epileptic subjects was performed in the form of two clusters. The result of epilepsy classification in this work is 96% success of 1D EEG data by using the features selected by the PCA method, 94% success of 2D MRI data using the selected features by applying the Forward Method, 100% classification accuracy of 1D EEG and 2D MRI datasets by LDA method using the obtained fused features . The article shows that the fused features of EEG-MRI can be used very effectively for the diagnosis of epilepsy.</p>
</abstract>

A 1-dimensional (1D) and 2-dimensional (2D) biomedical signal analysis based on the Discrete Cosine Transform (DCT) feature extraction method was performed to diagnose epilepsy disorders with high accuracy. For this purpose, Electroencephalogram (EEG) data were used for 1D signal analysis and Magnetic Resonance Imaging (MRI) data were used for 2D signal analysis. The feature vectors were obtained by applying 1D DCT together with statistical methods such as mean, variance, standard deviation, kurtosis, and skewness for EEG data and by applying 2D DCT together with the statistical method of mean for MRI data. The most useful features were selected by applying Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Forward Selection and Backward Selection methods to the obtained feature vectors. Using EEG stand-alone features, MRI stand-alone features and EEG-MRI fused features, the classification of healthy and epileptic subjects was performed in the form of two clusters. The result of epilepsy classification in this work is 96% success of 1D EEG data by using the features selected by the PCA method, 94% success of 2D MRI data using the selected features by applying the Forward Method, 100% classification accuracy of 1D EEG and 2D MRI datasets by LDA method using the obtained fused features . The article shows that the fused features of EEG-MRI can be used very effectively for the diagnosis of epilepsy.

A High-Performance Method Based on Features Fusion of EEG Brain Signal and MRI-Imaging Data for Epilepsy Classification

Department of Computer Engineering, Faculty of Engineering

Measurement Science Review

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

A 1-dimensional (1D) and 2-dimensional (2D) biomedical signal analysis based on the Discrete Cosine Transform (DCT) feature extraction method was performed to...

A High-Performance Method Based on Features Fusion of EEG Brain Signal and MRI-Imaging Data for Epilepsy Classification

Online veröffentlicht: 07. März 2024

Seitenbereich: 1 - 8

Eingereicht: 10. Mai 2023

Akzeptiert: 09. Jan. 2024

DOI: https://doi.org/10.2478/msr-2024-0001

Schlüsselwörter
Epilepsy, MRI, EEG, discrete cosine transform, medical image analysis, EEG-MRI classification

© 2024 Fatma Demirezen Yağmur et al., published by Sciendo

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

A High-Performance Method Based on Features Fusion of EEG Brain Signal and MRI-Imaging Data for Epilepsy Classification

Online veröffentlicht: 07. März 2024

Seitenbereich: 1 - 8

Eingereicht: 10. Mai 2023

Akzeptiert: 09. Jan. 2024

DOI: https://doi.org/10.2478/msr-2024-0001

SchlüsselwörterEpilepsy, MRI, EEG, discrete cosine transform, medical image analysis, EEG-MRI classification

© 2024 Fatma Demirezen Yağmur et al., published by Sciendo

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

Schlüsselwörter
Epilepsy, MRI, EEG, discrete cosine transform, medical image analysis, EEG-MRI classification