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Related Experiment Video

Updated: Jul 10, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
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BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

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Hilbert-domain sub-band feature framework for EEG-based seizure detection.

Abdullah Al Shiam1, Fahmid Al Farid2, Abu Saleh Musa Miah3

  • 1Department of Computer Science and Engineering, Netrokona University, Netrokona, Bangladesh.

Frontiers in Computational Neuroscience
|July 9, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an advanced method for detecting epileptic seizures using electroencephalogram (EEG) signals processed in the Hilbert domain. The novel approach achieves high classification accuracy, significantly improving epileptic seizure detection capabilities.

Area of Science:

  • Neurology
  • Biomedical Signal Processing
  • Machine Learning

Background:

  • Epilepsy is a neurological disorder characterized by abnormal brain activity.
  • Electroencephalogram (EEG) signal analysis is crucial for epilepsy diagnosis.
  • Existing machine learning methods for EEG classification have limitations.

Purpose of the Study:

  • To develop and evaluate a novel framework for epileptic seizure detection using EEG signals.
  • To process EEG signals in the Hilbert domain for enhanced feature extraction.
  • To improve the accuracy and reliability of automated epileptic seizure detection.

Main Methods:

  • EEG signals were segmented into frames and decomposed into sub-bands using Butterworth Bandpass Filters.
  • Hilbert Transform was applied to each sub-band, followed by extraction of entropy-based, spike-related, and statistical features.
Keywords:
EEG signal analysisHilbert TransformMRMR feature selectionentropy featuresepileptic seizure detectionmachine learning

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  • Minimum Redundancy Maximum Relevance (mRMR) feature selection was employed to identify discriminative features.
  • The framework was validated on the University of Bonn and CHB-MIT scalp EEG datasets using 15-fold cross-validation with various classifiers (SVM, RF, ET, LDA).

    • Main Results:

    • The proposed method achieved high classification accuracies of 99.27% (University of Bonn dataset) and 99.04% (CHB-MIT dataset).
    • Performance improvements of up to 1.43% were observed compared to state-of-the-art methods.
    • Statistical analyses (Friedman ANOVA, Tukey-Kramer) confirmed the robustness and reliability of the framework.

    Conclusions:

    • The Hilbert domain processing approach offers a highly effective method for epileptic seizure detection from EEG signals.
    • The feature extraction and selection strategy significantly enhances classification performance.
    • This framework demonstrates potential for real-world clinical application in epilepsy diagnosis and management.