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<p>This work describes a novel feature selection approach for detecting epileptic seizures in an EEG dataset that is based on a Hilbert similarity measurement of a convex set. Because the medical dataset has a high dimensionality, feature selection is vital for identifying diseases early and protecting human health. Moreover, high-dimensional data affect the prediction accuracy of machine learning algorithms and increase the system's complexity, making the results inefficient. Therefore, 1: This research presents a feature selection model mainly based on 2: Hilbert mathematical similarity measurement for computing similarity and related features based on high harmony inside the same feature. Using the proposed model, 3: The similarity between signals is computed and optimal, and 4: High similarity features are selected, while 5: Ineffective features are removed. Using the EEG data from Bonn University, we assessed the performance of the system. In addition, we assessed the proposed model based on recall, precision, and accuracy, which was compared to other earlier methods. Research findings confirmed that it was effective at extracting optimum features from EEG data. The proposed model obtained 100% accuracy within 10% of the feature selection.</p>

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Hilbert similarity convex for efficient EEG feature selections

  • 期刊论文

  • 发表日期：

    2025年4月12日

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  • Salwa Shakir Baawi,
  • Ekram Hakem,
  • Abdulkareem A. Al-Hamzawi,
  • Dhiah Al-Shammary,
  • Ayman Ibaida,
  • Ahmed M. Mahdi

摘要

This work describes a novel feature selection approach for detecting epileptic seizures in an EEG dataset that is based on a Hilbert similarity measurement of a convex set. Because the medical dataset has a high dimensionality, feature selection is vital for identifying diseases early and protecting human health. Moreover, high-dimensional data affect the prediction accuracy of machine learning algorithms and increase the system's complexity, making the results inefficient. Therefore, 1: This research presents a feature selection model mainly based on 2: Hilbert mathematical similarity measurement for computing similarity and related features based on high harmony inside the same feature. Using the proposed model, 3: The similarity between signals is computed and optimal, and 4: High similarity features are selected, while 5: Ineffective features are removed. Using the EEG data from Bonn University, we assessed the performance of the system. In addition, we assessed the proposed model based on recall, precision, and accuracy, which was compared to other earlier methods. Research findings confirmed that it was effective at extracting optimum features from EEG data. The proposed model obtained 100% accuracy within 10% of the feature selection.