A novel dual-branch network for comprehensive spatiotemporal information integration for EEG-based epileptic seizure detection
Xiaobing Deng, Yuvaraj Rajamanickam, Yuvaraj Rajamanickam, Yuvaraj Rajamanickam, Yuvaraj Rajamanickam
TL;DR
This paper introduces a new dual-branch network to accurately detect epileptic seizures using EEG data by better integrating spatial and temporal information.
Contribution
The novel Deepwalk-TS model combines spatiotemporal fusion strategies to improve seizure detection accuracy.
Findings
The Deepwalk-TS model achieved 99.54% accuracy in seizure detection.
The model outperforms existing methods in capturing spatial and temporal EEG relationships.
Abstract
Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal brain activity, which can severely affects people’s normal lives. To improve the lives of these patients, it is necessary to develop accurate methods to predict seizures. Electroencephalography (EEG), as a non-invasive and real-time technique, is crucial for the early diagnosis of epileptic seizures by monitoring abnormal brain activity associated with seizures. Deep learning EEG-based detection methods have made significant progress, but still face challenges such as the underutilization of spatial relationships, inter-individual physiological variability, and sequence intricacies. To tackle these challenges, we introduce the Dual-Branch Deepwalk-Transformer Spatiotemporal Fusion Network (Deepwalk-TS), which effectively integrates spatiotemporal information from EEG signals to enable accurate and…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Blind Source Separation Techniques · Epilepsy research and treatment