Comparative analysis of VEP signals discrimination methods based on time-frequency transformation and CNN-2D
The Visual Evoked Potential (VEP) examination is used to analyze the appropriate functioning of the optical pathways from the retina to the visual cortex. In hospitals, the diagnosis made by physicians is based mainly on reading the temporal trace and identifying the latency P100. However, after a c...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Biomedical Engineering Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667099224000033 |
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| author | Zineb Cheker Saad Chakkor Ahmed EL Oualkadi Mostafa Baghouri Rachid Belfkih Jalil Abdelkader El Hangouche Jawhar Laameche |
| author_facet | Zineb Cheker Saad Chakkor Ahmed EL Oualkadi Mostafa Baghouri Rachid Belfkih Jalil Abdelkader El Hangouche Jawhar Laameche |
| author_sort | Zineb Cheker |
| collection | DOAJ |
| description | The Visual Evoked Potential (VEP) examination is used to analyze the appropriate functioning of the optical pathways from the retina to the visual cortex. In hospitals, the diagnosis made by physicians is based mainly on reading the temporal trace and identifying the latency P100. However, after a considerable research effort, it has been confirmed that this method is subjective and relatively less reliable. In our work, we report different approaches to resolve the inadequacy of traditional classification, by studying the efficiency of VEP signal classification in a comparative approach using 3 models: Model A: STFT-CNN, Model B: CWT-CNN, and Model C: Wigner-Ville-CNN, therefore we evaluate in the same context the effectiveness of using a pre-trained 2D CNN structure. The time-frequency transformation allows us to generate two-dimensional data from one-dimensional signals to bring out the integrated features that are not valued in the temporal plot, and then exploit them for good discrimination between the two classes, in order to be able to use a CNN-2D classification architecture, taking into consideration the advantages offered by this architecture in terms of the involvement of the attribute extraction phase and its efficiency in classifying 2D data. The results provided by the different scenarios proved that the Wigner-Ville transformation combined with a pre-trained CNN architecture can be considered a good method in terms of different performance metrics, which demonstrates that it is a successful candidate for providing significant assistance to physicians in their analysis of VEP signals. |
| first_indexed | 2024-04-24T20:25:01Z |
| format | Article |
| id | doaj.art-9f618c2c39d642c899527930caeefeab |
| institution | Directory Open Access Journal |
| issn | 2667-0992 |
| language | English |
| last_indexed | 2024-04-24T20:25:01Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomedical Engineering Advances |
| spelling | doaj.art-9f618c2c39d642c899527930caeefeab2024-03-22T05:41:06ZengElsevierBiomedical Engineering Advances2667-09922024-06-017100114Comparative analysis of VEP signals discrimination methods based on time-frequency transformation and CNN-2DZineb Cheker0Saad Chakkor1Ahmed EL Oualkadi2Mostafa Baghouri3Rachid Belfkih4Jalil Abdelkader El Hangouche5Jawhar Laameche6Laboratoire d'Ingénierie des Systèmes Innovants (ISI), National School of Applied Sciences of Tétouan (ENSATe), University of Abdelmalek Essaadi, Morocco; Corresponding author.Laboratory of Information and Communication Technologies (LabTIC), Ecole Nationale des Sciences Appliquées de Tanger, MoroccoLaboratoire d'Ingénierie des Systèmes Innovants (ISI), National School of Applied Sciences of Tétouan (ENSATe), University of Abdelmalek Essaadi, MoroccoSmart Materials and Artificial intelligence Team, LCCPS, École Nationale Superieur des Arts et Métiers de Casablanca, MoroccoFaculty of Medicine and Pharmacy, Laboratory of Neurology, Tangier, MoroccoFaculty of Medicine and Pharmacy, Laboratory of Physiology, Tangier, MoroccoFaculty of Medicine and Pharmacy, Laboratory of Chemistry and Biochemistry, Tangier, MoroccoThe Visual Evoked Potential (VEP) examination is used to analyze the appropriate functioning of the optical pathways from the retina to the visual cortex. In hospitals, the diagnosis made by physicians is based mainly on reading the temporal trace and identifying the latency P100. However, after a considerable research effort, it has been confirmed that this method is subjective and relatively less reliable. In our work, we report different approaches to resolve the inadequacy of traditional classification, by studying the efficiency of VEP signal classification in a comparative approach using 3 models: Model A: STFT-CNN, Model B: CWT-CNN, and Model C: Wigner-Ville-CNN, therefore we evaluate in the same context the effectiveness of using a pre-trained 2D CNN structure. The time-frequency transformation allows us to generate two-dimensional data from one-dimensional signals to bring out the integrated features that are not valued in the temporal plot, and then exploit them for good discrimination between the two classes, in order to be able to use a CNN-2D classification architecture, taking into consideration the advantages offered by this architecture in terms of the involvement of the attribute extraction phase and its efficiency in classifying 2D data. The results provided by the different scenarios proved that the Wigner-Ville transformation combined with a pre-trained CNN architecture can be considered a good method in terms of different performance metrics, which demonstrates that it is a successful candidate for providing significant assistance to physicians in their analysis of VEP signals.http://www.sciencedirect.com/science/article/pii/S2667099224000033VEPLatency P100STFTCWTWigner-VilleCNN-2D |
| spellingShingle | Zineb Cheker Saad Chakkor Ahmed EL Oualkadi Mostafa Baghouri Rachid Belfkih Jalil Abdelkader El Hangouche Jawhar Laameche Comparative analysis of VEP signals discrimination methods based on time-frequency transformation and CNN-2D Biomedical Engineering Advances VEP Latency P100 STFT CWT Wigner-Ville CNN-2D |
| title | Comparative analysis of VEP signals discrimination methods based on time-frequency transformation and CNN-2D |
| title_full | Comparative analysis of VEP signals discrimination methods based on time-frequency transformation and CNN-2D |
| title_fullStr | Comparative analysis of VEP signals discrimination methods based on time-frequency transformation and CNN-2D |
| title_full_unstemmed | Comparative analysis of VEP signals discrimination methods based on time-frequency transformation and CNN-2D |
| title_short | Comparative analysis of VEP signals discrimination methods based on time-frequency transformation and CNN-2D |
| title_sort | comparative analysis of vep signals discrimination methods based on time frequency transformation and cnn 2d |
| topic | VEP Latency P100 STFT CWT Wigner-Ville CNN-2D |
| url | http://www.sciencedirect.com/science/article/pii/S2667099224000033 |
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