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Related Concept Videos

Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

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Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
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Focal Seizures
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Related Experiment Video

Updated: Jan 13, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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EEG Complexity Analysis of Psychogenic Non-Epileptic and Epileptic Seizures Using Entropy and Machine Learning.

Hesam Shokouh Alaei1, Samaneh Kouchaki2, Mahinda Yogarajah3

  • 1Centre for Biomedical Engineering, School of Engineering, University of Surrey, Guildford GU2 7XH, UK.

Entropy (Basel, Switzerland)
|October 28, 2025
PubMed
Summary

This study shows that analyzing brainwave patterns using entropy measures can help distinguish psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES). The dynamic state analysis of entropy offers a promising approach for accurate diagnosis.

Keywords:
entropyepileptic seizuresmachine learningpreictal and interictal analysispsychogenic non-epileptic seizures

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Psychogenic non-epileptic seizures (PNES) are frequently misdiagnosed as epileptic seizures (ES).
  • Misdiagnosis leads to ineffective treatments and delayed psychological support.
  • Accurate differentiation between PNES and ES is crucial for patient care.

Purpose of the Study:

  • To investigate the utility of nine entropy measures derived from electroencephalogram (EEG) data for classifying PNES from ES.
  • To evaluate the diagnostic performance of these entropy measures in both interictal and preictal states, as well as a novel dynamic state.

Main Methods:

  • EEG data from 74 patients (46 PNES, 28 ES) were analyzed using one-minute preictal and interictal recordings.
  • Nine entropy measures were calculated and used with various machine learning algorithms (k-NN, Naïve Bayes, LDA, LR, SVM, RF, MLP, XGBoost) under a leave-one-subject-out cross-validation.
  • A dynamic state was defined as the difference in entropy between interictal and preictal periods.

Main Results:

  • Entropy measures, particularly Sample, Fuzzy, Conditional, and Dispersion entropy, showed distinct patterns between PNES and ES.
  • Classification performance was highest in the dynamic state, with Fuzzy entropy and SVM achieving a balanced accuracy of 72.4%.
  • Key discriminative EEG channels (O1, O2, T5, F7, Pz) were identified.

Conclusions:

  • Entropy analysis of EEG signals, especially in the dynamic state, shows significant potential for differentiating PNES from ES.
  • This approach could improve diagnostic accuracy and guide appropriate treatment strategies.
  • Fuzzy entropy combined with SVM demonstrated robust classification performance.