Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model

• Published in: Diagnostics (Basel) Vol. 15; no. 1; p. 84
• Main Authors: Dişli, Fırat, Gedikpınar, Mehmet, Fırat, Hüseyin, Şengür, Abdulkadir, Güldemir, Hanifi, Koundal, Deepika
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2025; MDPI
• Subjects: Accuracy; Algorithms; Automation; Classification; continuous wavelet transform; Convulsions & seizures; Datasets; Deep learning; depthwise convolution; Electroencephalography; Entropy; Epilepsy; image concatenate; Machine learning; Neural networks; Seizures (Medicine); Support vector machines; Time series; Tomography; Wavelet transforms; Turkey; epilepsy; depthwise convolution; image concatenate; continuous wavelet transform
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics15010084

Background/Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.