Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals
arXiv:2604.00163v2 Announce Type: replace-cross Abstract: Epileptic seizures are neurological disorders characterized by abnormal and excessive electrical activity in the brain, resulting in recurrent seizure events. Electroencephalogram (EEG) signals are widely used for seizure diagnosis due to their ability to capture temporal and spatial neural dynamics. While recent deep learning methods have achieved high detection accuracy, they often lack interpretability and neurophysiological relevance. This study presents a frequency-aware framework for epileptic seizure detection based on ictal-phas
The proliferation of advanced AI techniques, particularly deep learning and GCNs, is enabling breakthroughs in complex signal processing previously unattainable with traditional methods.
This development indicates a growing capability for AI to improve diagnostic accuracy and interpretability in critical medical fields, impacting healthcare delivery and AI's role within it.
The explicit focus on frequency-aware analysis and interpretability, alongside high accuracy, addresses key limitations of previous deep learning approaches in medical diagnostics.
- · AI healthcare diagnostic companies
- · Epilepsy patients
- · Medical AI researchers
- · Neurology departments
- · Traditional EEG diagnostic methods
- · Companies offering less accurate diagnostic tools
Improved early and accurate detection of epileptic seizures leading to better patient outcomes.
Increased adoption of AI diagnostic tools in clinical settings, especially for interpreting complex neurological data.
The development of personalized and predictive treatment protocols for neurological disorders based on highly granular AI-driven diagnostic insights.
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Read at arXiv cs.AI