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DSLWNet: a dual-stream lightweight deep learning network for the detection of Epileptic seizures using EEG signals.

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Title: DSLWNet: a dual-stream lightweight deep learning network for the detection of Epileptic seizures using EEG signals.
Authors: Silpa, Bommala (AUTHOR), Hota, Malaya Kumar (AUTHOR)
Source: Connection Science. Dec 2025, Vol. 37 Issue 1, p1-33. 33p.
Subjects: Epilepsy, Seizures (Medicine), Neurophysiology, Spectrograms, Support vector machines, Recurrent neural networks, Convolutional neural networks, Deep learning
Abstract: Epilepsy is one of the most prevalent neurological disorders, and the accurate detection of epileptic seizures is challenging. Therefore, a dual-stream deep learning network is proposed in this research to extract the deep features by utilising the scalograms and time-series EEG signals. In this, first, the convolutional neural network (CNN) extracts the spatial dimensional features from the scalogram images, and then the squeeze and excitation (SE) technique enhances the relevant informative features by adjusting the channel weights. Correspondingly, the gated recurrent unit (GRU) extracts the temporal characteristics from the time-series EEG signal, and then for assigning more weights to the significant features the confined attention (CA) mechanism is included. Next, the extracted features are fused to form a deep feature set for the accurate detection of seizures using the support vector machine (SVM) classifier. Further, to improve the seizure detection rate, the regression at the end of variational mode extraction techniques (VME) is employed in the preprocessing stage. In addition, the performance of the proposed dual-stream lightweight seizure network (DSLWNet) is evaluated using the CHB-MIT and Bonn datasets. The experimental outcomes show the superiority of the proposed work in seizure detection by achieving an accuracy of 98.67% and 99.5%, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: DSLWNet: a dual-stream lightweight deep learning network for the detection of Epileptic seizures using EEG signals.
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  Data: <searchLink fieldCode="AR" term="%22Silpa%2C+Bommala%22">Silpa, Bommala</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hota%2C+Malaya+Kumar%22">Hota, Malaya Kumar</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Connection+Science%22">Connection Science</searchLink>. Dec 2025, Vol. 37 Issue 1, p1-33. 33p.
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  Data: <searchLink fieldCode="DE" term="%22Epilepsy%22">Epilepsy</searchLink><br /><searchLink fieldCode="DE" term="%22Seizures+%28Medicine%29%22">Seizures (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Neurophysiology%22">Neurophysiology</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrograms%22">Spectrograms</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: Epilepsy is one of the most prevalent neurological disorders, and the accurate detection of epileptic seizures is challenging. Therefore, a dual-stream deep learning network is proposed in this research to extract the deep features by utilising the scalograms and time-series EEG signals. In this, first, the convolutional neural network (CNN) extracts the spatial dimensional features from the scalogram images, and then the squeeze and excitation (SE) technique enhances the relevant informative features by adjusting the channel weights. Correspondingly, the gated recurrent unit (GRU) extracts the temporal characteristics from the time-series EEG signal, and then for assigning more weights to the significant features the confined attention (CA) mechanism is included. Next, the extracted features are fused to form a deep feature set for the accurate detection of seizures using the support vector machine (SVM) classifier. Further, to improve the seizure detection rate, the regression at the end of variational mode extraction techniques (VME) is employed in the preprocessing stage. In addition, the performance of the proposed dual-stream lightweight seizure network (DSLWNet) is evaluated using the CHB-MIT and Bonn datasets. The experimental outcomes show the superiority of the proposed work in seizure detection by achieving an accuracy of 98.67% and 99.5%, respectively. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/09540091.2025.2518985
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        Text: English
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        Type: general
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      – SubjectFull: Neurophysiology
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      – SubjectFull: Convolutional neural networks
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      – SubjectFull: Deep learning
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      – TitleFull: DSLWNet: a dual-stream lightweight deep learning network for the detection of Epileptic seizures using EEG signals.
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              Text: Dec 2025
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