Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model
<b>Background/Objectives:</b> Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly...
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MDPI AG
2025-01-01
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| Series: | Diagnostics |
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| author | Fırat Dişli Mehmet Gedikpınar Hüseyin Fırat Abdulkadir Şengür Hanifi Güldemir Deepika Koundal |
| author_facet | Fırat Dişli Mehmet Gedikpınar Hüseyin Fırat Abdulkadir Şengür Hanifi Güldemir Deepika Koundal |
| author_sort | Fırat Dişli |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly important. Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction. <b>Methods:</b> This study proposes a continuous wavelet transform-based depthwise convolutional neural network (DCNN) for epilepsy diagnosis. The 35-channel EEG signals were transformed into 35-channel images using continuous wavelet transform. These images were then concatenated horizontally and vertically into a single image (seven rows by five columns) using Python’s PIL library, which served as input for training the DCNN model. <b>Results:</b> The proposed model achieved impressive performance metrics on unseen test data: 95.99% accuracy, 94.27% sensitivity, 97.29% specificity, and 96.34% precision. Comparative analyses with previous studies and state-of-the-art models demonstrated the superior performance of the DCNN model and image concatenation technique. <b>Conclusions:</b> Unlike earlier works, this approach did not employ additional classifiers or feature selection algorithms. The developed model and image concatenation method offer a novel methodology for epilepsy diagnosis that can be extended to different datasets, potentially providing a valuable tool to support neurologists globally. |
| format | Article |
| id | doaj-art-f1849290a3ac4e609d4a7f03efe53c85 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-f1849290a3ac4e609d4a7f03efe53c852025-01-10T13:16:40ZengMDPI AGDiagnostics2075-44182025-01-011518410.3390/diagnostics15010084Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network ModelFırat Dişli0Mehmet Gedikpınar1Hüseyin Fırat2Abdulkadir Şengür3Hanifi Güldemir4Deepika Koundal5Department of Electrical and Electronic Engineering, Faculty of Technology, Firat University, 23000 Elazig, TurkeyDepartment of Electrical and Electronic Engineering, Faculty of Technology, Firat University, 23000 Elazig, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Dicle University, 21000 Diyarbakir, TurkeyDepartment of Electrical and Electronic Engineering, Faculty of Technology, Firat University, 23000 Elazig, TurkeyDepartment of Electrical and Electronic Engineering, Faculty of Technology, Firat University, 23000 Elazig, TurkeyA.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland<b>Background/Objectives:</b> Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly important. Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction. <b>Methods:</b> This study proposes a continuous wavelet transform-based depthwise convolutional neural network (DCNN) for epilepsy diagnosis. The 35-channel EEG signals were transformed into 35-channel images using continuous wavelet transform. These images were then concatenated horizontally and vertically into a single image (seven rows by five columns) using Python’s PIL library, which served as input for training the DCNN model. <b>Results:</b> The proposed model achieved impressive performance metrics on unseen test data: 95.99% accuracy, 94.27% sensitivity, 97.29% specificity, and 96.34% precision. Comparative analyses with previous studies and state-of-the-art models demonstrated the superior performance of the DCNN model and image concatenation technique. <b>Conclusions:</b> Unlike earlier works, this approach did not employ additional classifiers or feature selection algorithms. The developed model and image concatenation method offer a novel methodology for epilepsy diagnosis that can be extended to different datasets, potentially providing a valuable tool to support neurologists globally.https://www.mdpi.com/2075-4418/15/1/84epilepsydepthwise convolutionimage concatenatecontinuous wavelet transform |
| spellingShingle | Fırat Dişli Mehmet Gedikpınar Hüseyin Fırat Abdulkadir Şengür Hanifi Güldemir Deepika Koundal Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model Diagnostics epilepsy depthwise convolution image concatenate continuous wavelet transform |
| title | Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model |
| title_full | Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model |
| title_fullStr | Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model |
| title_full_unstemmed | Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model |
| title_short | Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model |
| title_sort | epilepsy diagnosis from eeg signals using continuous wavelet transform based depthwise convolutional neural network model |
| topic | epilepsy depthwise convolution image concatenate continuous wavelet transform |
| url | https://www.mdpi.com/2075-4418/15/1/84 |
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