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Related Experiment Video

Updated: Dec 14, 2025

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
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Automatic seizure detection using neutrosophic classifier.

Abdul Quaiyum Ansari1, Priyanka Sharma2, Manjari Tripathi3

  • 1Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India.

Physical and Engineering Sciences in Medicine
|July 23, 2020
PubMed
Summary

This study introduces a novel algorithm for automatic seizure detection using electroencephalogram (EEG) signals. The new method combines frequency-domain features with a neutrosophic logic-based k-NN classifier, improving detection accuracy and reliability.

Keywords:
Alpha-delta ratio (ADR)Neutrosophic logicSeizure detectionk-NN classifier

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Seizures represent a common neurological dysfunction requiring timely detection and treatment.
  • Electroencephalogram (EEG) signal analysis is crucial for diagnosing and managing seizures.
  • Automatic seizure detection from EEG signals is an active research area with ongoing challenges in reliability and precision.

Purpose of the Study:

  • To propose a novel algorithm for automatic seizure detection from EEG signals.
  • To enhance the reliability and precision of seizure detection systems.
  • To introduce a neutrosophic logic-based k-nearest neighbor (NL-k-NN) classifier for EEG signal analysis.

Main Methods:

  • Extraction of frequency-domain features from EEG signals.
  • Development and application of a neutrosophic logic-based k-nearest neighbor (NL-k-NN) classifier.
  • Validation of the proposed algorithm using EEG datasets from AIIMS, Bonn University, and CHB-MIT.

Main Results:

  • The proposed algorithm achieved high classification accuracies: 98.16% (AIIMS), 100% (Bonn University), and 89.06% (CHB-MIT).
  • The NL-k-NN classifier demonstrated superior performance compared to traditional k-NN, LDA, and SVM classifiers.
  • Consistent performance was observed across different EEG datasets, indicating robustness.

Conclusions:

  • The developed algorithm, integrating frequency-domain features and NL-k-NN, offers a reliable and precise method for automatic seizure detection.
  • The novel neutrosophic classifier contributes significantly to advancing seizure detection technology.
  • This approach holds promise for improved early diagnosis and treatment of patients with seizures.