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Withdrawn paper details GCN-based EEG seizure detection

By PulseAugur Editorial · [1 sources] ·

A research paper, now withdrawn, proposed a framework for detecting epileptic seizures using Graph Convolutional Neural Networks (GCNs) applied to electroencephalogram (EEG) signals. The method involved decomposing EEG signals into five frequency bands and extracting features before feeding them into a GCN to model spatial dependencies. Experiments on the CHB-MIT dataset showed high accuracy, particularly in mid-frequency bands, suggesting improved interpretability and diagnostic precision over traditional broadband methods. AI

RANK_REASON The cluster contains a withdrawn academic paper detailing a novel research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Withdrawn paper details GCN-based EEG seizure detection

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The cluster contains a withdrawn academic paper detailing a novel research methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ferdaus Anam Jibon, Fazlul Hasan Siddiqui, F. Deeba, Gahangir Hossain ·

    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 …

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