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<p>Epilepsy is a prevalent neurodevelopmental illness which can lead to serious, even fatal events. Early detection of Epileptic Seizures (ES) can reduce all risks related to epilepsy and contribute to treating people living with this condition. Detection of ES through electroencephalography (EEG) is of great importance in the treatment of epilepsy as well as in diagnostic measures of medicine. Thus, this paper intends to design a hybrid technique that effectively detects ES from a unique amalgamation of traditional Machine learning(ML) and Deep learning(DL) models integrated with the Stacking ensemble classifier for precise seizure categorization. The investigational outcomes demonstrated that the hybrid method can precisely classify ES by reporting an accuracy equivalent to 98.7%, which could aid in improving the diagnostic procedure for brain-dysfunctional patients.</p>

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A Hybrid Technique for Enhanced Epileptic Seizure Detection using EEG Signals

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Sonam Khattar,
  • Ravinder Kaur,
  • Kanika Sharma

摘要

Epilepsy is a prevalent neurodevelopmental illness which can lead to serious, even fatal events. Early detection of Epileptic Seizures (ES) can reduce all risks related to epilepsy and contribute to treating people living with this condition. Detection of ES through electroencephalography (EEG) is of great importance in the treatment of epilepsy as well as in diagnostic measures of medicine. Thus, this paper intends to design a hybrid technique that effectively detects ES from a unique amalgamation of traditional Machine learning(ML) and Deep learning(DL) models integrated with the Stacking ensemble classifier for precise seizure categorization. The investigational outcomes demonstrated that the hybrid method can precisely classify ES by reporting an accuracy equivalent to 98.7%, which could aid in improving the diagnostic procedure for brain-dysfunctional patients.