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Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

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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Related Experiment Video

Updated: Jun 25, 2026

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates
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TATPat based explainable EEG model for neonatal seizure detection.

Turker Tuncer1, Sengul Dogan2, Irem Tasci3

  • 1Department of Digital Forensics Engineering, College of Technology, Firat University, 23119, Elazig, Turkey.

Scientific Reports
|November 4, 2024
PubMed
Summary

This study introduces an explainable feature engineering (EFE) model for detecting neonatal seizures from electroencephalography (EEG) signals. The model achieves high accuracy, offering insights into seizure causes through Directed Lobish (DLob).

Keywords:
Causal connectome theoryDLobV2EEG signal classificationNeonatal epileptic seizure detectionTATPat

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Artificial Intelligence

Background:

  • Electroencephalography (EEG) is a cost-effective method for brain data collection.
  • EEG signal processing is crucial for neuroscience and machine learning (ML).
  • Detecting neonatal seizures is vital for infant health.

Purpose of the Study:

  • To detect and explain neonatal seizures using an explainable feature engineering (EFE) model.
  • To propose a novel EFE model incorporating Directed Lobish (DLob) for explainability.
  • To classify neonatal EEG signals with high accuracy and provide interpretable results.

Main Methods:

  • Developed a novel EFE model with four phases: feature extraction (TATPat), feature selection (CWNCA), explainable result generation (DLob/CCT), and classification (tSVM).
  • Utilized a channel transformer and automaton to extract features (TATPat) from 19-channel neonatal EEG data, generating 3249 features.
  • Employed cumulative weight-based neighborhood component analysis (CWNCA) for feature selection and a t-algorithm-based support vector machine (tSVM) for classification.

Main Results:

  • The TATPat feature extraction method generated 3249 features per EEG segment.
  • The EFE model achieved 99.15% accuracy with 10-fold cross-validation (CV) and 76.37% with leave-one subject-out (LOSO) CV.
  • The model successfully generated DLob strings for explainable seizure detection.

Conclusions:

  • The TATPat-based EFE model demonstrates strong performance in classifying neonatal EEG signals.
  • The proposed model is effective for explainable artificial intelligence (XAI) in neuroscience.
  • This approach offers a valuable tool for understanding and detecting neonatal seizures.