An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Ziaullah Khan, Aakanksha Dayal, Hee-Cheol Kim
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
EEG signal processing
3D convolutional neural network (3D-CNN)
biomedical signal analysis
seizure detection
self-attention mechanism
short-time Fourier transform
Online Access:https://ieeexplore.ieee.org/document/11017574/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2169-3536

Similar Items