An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection
Epilepsy is a widespread neurological disorder affecting approximately 50 million individuals globally, with a disproportionately high burden in low- and middle-income countries. It is characterized by recurrent seizures caused by sudden and uncontrolled electrical discharges in brain cells, often l...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11017574/ |
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| author | Ziaullah Khan Aakanksha Dayal Hee-Cheol Kim |
| author_facet | Ziaullah Khan Aakanksha Dayal Hee-Cheol Kim |
| author_sort | Ziaullah Khan |
| collection | DOAJ |
| description | Epilepsy is a widespread neurological disorder affecting approximately 50 million individuals globally, with a disproportionately high burden in low- and middle-income countries. It is characterized by recurrent seizures caused by sudden and uncontrolled electrical discharges in brain cells, often leading to cognitive and motor impairments. Electroencephalography (EEG) remains the gold standard for epilepsy diagnosis, offering non-invasive monitoring of brain activity. However, the complexity and variability of epileptic patterns make traditional visual analysis subjective, time-consuming, and impractical for continuous monitoring. This underscores the need for automated deep learning solutions to improve diagnostic accuracy and operational efficiency. This study introduces a novel 3D Convolutional Neural Network with integrated Self-Attention (3D-ACNN), specifically designed for the volumetric analysis of multi-channel EEG data. The proposed algorithm preprocesses 21-channel EEG signals by segmenting them into 4-second Hann windows, applies the Short-Time Fourier Transform (STFT) to extract time-frequency features, and constructs stacked spectrograms that serve as three-dimensional inputs to the model. The proposed model (3D-ACNN), integrated with self-attention mechanisms, effectively captures spatiotemporal dependencies, enhancing seizure classification accuracy. The model achieved a classification accuracy of 99.89. |
| format | Article |
| id | doaj-art-77fb7b20a1f343cab5a053f092f835fa |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-77fb7b20a1f343cab5a053f092f835fa2025-08-20T03:51:03ZengIEEEIEEE Access2169-35362025-01-011312043712044510.1109/ACCESS.2025.357464611017574An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy DetectionZiaullah Khan0https://orcid.org/0009-0008-3518-3712Aakanksha Dayal1https://orcid.org/0009-0005-4026-9615Hee-Cheol Kim2https://orcid.org/0000-0002-5399-7647Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, South KoreaDepartment of Computer Engineering, Inje University, Gimhae, South KoreaDigital Anti-Aging Healthcare, College of AI Convergence, u-AHRC, Inje University, Gimhae, South KoreaEpilepsy is a widespread neurological disorder affecting approximately 50 million individuals globally, with a disproportionately high burden in low- and middle-income countries. It is characterized by recurrent seizures caused by sudden and uncontrolled electrical discharges in brain cells, often leading to cognitive and motor impairments. Electroencephalography (EEG) remains the gold standard for epilepsy diagnosis, offering non-invasive monitoring of brain activity. However, the complexity and variability of epileptic patterns make traditional visual analysis subjective, time-consuming, and impractical for continuous monitoring. This underscores the need for automated deep learning solutions to improve diagnostic accuracy and operational efficiency. This study introduces a novel 3D Convolutional Neural Network with integrated Self-Attention (3D-ACNN), specifically designed for the volumetric analysis of multi-channel EEG data. The proposed algorithm preprocesses 21-channel EEG signals by segmenting them into 4-second Hann windows, applies the Short-Time Fourier Transform (STFT) to extract time-frequency features, and constructs stacked spectrograms that serve as three-dimensional inputs to the model. The proposed model (3D-ACNN), integrated with self-attention mechanisms, effectively captures spatiotemporal dependencies, enhancing seizure classification accuracy. The model achieved a classification accuracy of 99.89.https://ieeexplore.ieee.org/document/11017574/EEG signal processing3D convolutional neural network (3D-CNN)biomedical signal analysisseizure detectionself-attention mechanismshort-time Fourier transform |
| spellingShingle | Ziaullah Khan Aakanksha Dayal Hee-Cheol Kim An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection IEEE Access EEG signal processing 3D convolutional neural network (3D-CNN) biomedical signal analysis seizure detection self-attention mechanism short-time Fourier transform |
| title | An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection |
| title_full | An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection |
| title_fullStr | An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection |
| title_full_unstemmed | An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection |
| title_short | An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection |
| title_sort | attention enhanced 3d cnn framework for spectrogram based eeg analysis in epilepsy detection |
| topic | EEG signal processing 3D convolutional neural network (3D-CNN) biomedical signal analysis seizure detection self-attention mechanism short-time Fourier transform |
| url | https://ieeexplore.ieee.org/document/11017574/ |
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