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Epilepsy is very sensitive and severe neurological disorder that may affect human lives. The EEG is the very important tool to identify the Epilepsy information. In this paper a new combination of frame work is suggested to identify the epileptic seizure from the EEG signal. This proposed research work improves the accuracy of the seizure and reduce the computational time. The validate this theoretical studies, DWT Sym8 mother wavelets are used to detect the epileptic features from EEG signal. A Particle Swarm Optimization is used to identify the best features of epileptic. To identify the features into epileptic and non-epileptic signal a machine learning classifier are proposed. The proposed methodology is tested with Boon University datasets of A and E. Also, a comparison is made with state of art in the existing literature results.

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Hybrid Framework for Epileptic Seizure Detection Using Wavelets and ML Classifiers

  • 会议论文

  • 发表日期：

    2026年

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  • D. U. Latha,
  • D. N. Varshitha,
  • Y. M. Manu,
  • K. B. Shalini,
  • A. Jayanthiladevi

摘要

Epilepsy is very sensitive and severe neurological disorder that may affect human lives. The EEG is the very important tool to identify the Epilepsy information. In this paper a new combination of frame work is suggested to identify the epileptic seizure from the EEG signal. This proposed research work improves the accuracy of the seizure and reduce the computational time. The validate this theoretical studies, DWT Sym8 mother wavelets are used to detect the epileptic features from EEG signal. A Particle Swarm Optimization is used to identify the best features of epileptic. To identify the features into epileptic and non-epileptic signal a machine learning classifier are proposed. The proposed methodology is tested with Boon University datasets of A and E. Also, a comparison is made with state of art in the existing literature results.