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Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

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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.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
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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.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
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Related Experiment Video

Updated: Dec 17, 2025

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
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Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

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Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform.

Deba Prasad Dash1, Maheshkumar H Kolekar1

  • 1Department of Electrical Engineering, Indian Institute of Technology Patna, Bihta, Bihar 801103, India.

Journal of Biomedical Research
|June 21, 2020
PubMed
Summary

This study introduces an efficient algorithm for automatic seizure detection using electroencephalogram (EEG) signals. The developed method achieves 100% accuracy in classifying healthy and seizure EEG data, offering a robust tool for neurological disorder diagnosis.

Keywords:
electroencephalogramentropyepilepsyhidden Markov modelseizuretunable Q wavelet transform

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Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Epilepsy is a widespread neurological disorder affecting 70 million globally.
  • Electroencephalogram (EEG) is a noninvasive method to record brain activity.
  • Accurate seizure detection is crucial for patient diagnosis and management.

Purpose of the Study:

  • To design an efficient algorithm for automatic seizure detection using EEG signals.
  • To extract and fuse relevant features from EEG signals for improved classification accuracy.
  • To evaluate the performance of the proposed algorithm in distinguishing between healthy and seizure EEG patterns.

Main Methods:

  • EEG signals were analyzed using tunable Q wavelet transform for subband decomposition.
  • Shannon entropy, collision entropy, transfer entropy, conditional probability, and Hjorth parameters were extracted as features.
  • Discriminant correlation analysis was employed for feature fusion.
  • Kruskal-Wallis test was used to select optimal decomposition levels.
  • Hidden Markov Model was utilized for classification.

Main Results:

  • The proposed algorithm achieved 100% accuracy in classifying healthy versus seizure EEG signals.
  • Transfer entropy was identified as a significant feature for class discrimination.
  • An accuracy of 96.87% was achieved in classifying surface seizure and non-seizure EEG segments.
  • Optimal performance was observed with Q=2 and J=10 decomposition levels.

Conclusions:

  • The developed algorithm provides an efficient and robust method for automatic seizure detection from EEG.
  • The fusion of multiple features enhances classification performance.
  • The approach demonstrates high accuracy and reduced computation time, making it suitable for clinical applications.