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<p>This paper presents an efficient feature selection based on Ruzicka similarity to detect and diagnoses seizures caused by epilepsy. The proposed approach reduces the feature space while retaining the most relevant features for classification, enhancing the performance of standard Machine Learning (ML) classifiers. Technically, Bonn University EEG dataset is utilized to validate the model, and classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF) are applied. Several measures such as accuracy, recall, precision, and F1-score have been applied for the model evaluation. Results demonstrate that the proposed Ruzicka-based feature selection method achieves superior classification accuracy of 100% with DT, NB, and RF for binary class combinations, outperforming other feature selection strategies. The Ruzicka-based feature selection reduces the feature space from 4097 to 1229 features (30% selection ratio) for the 23.6-s Electroencephalogram (EEG) signal while maintaining high classification accuracy. These findings highlight the potential of the proposed approach to improve diagnostic accuracy in medical applications. </p>

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Efficient feature selection based on Ruzicka similarity for EEG diagnosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月19日

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  • Sarah L. Alzamili,
  • Salwa Shakir Baawi,
  • Mustafa Noaman Kadhim,
  • Dhiah Al-Shammary,
  • Ayman Ibaida

摘要

This paper presents an efficient feature selection based on Ruzicka similarity to detect and diagnoses seizures caused by epilepsy. The proposed approach reduces the feature space while retaining the most relevant features for classification, enhancing the performance of standard Machine Learning (ML) classifiers. Technically, Bonn University EEG dataset is utilized to validate the model, and classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF) are applied. Several measures such as accuracy, recall, precision, and F1-score have been applied for the model evaluation. Results demonstrate that the proposed Ruzicka-based feature selection method achieves superior classification accuracy of 100% with DT, NB, and RF for binary class combinations, outperforming other feature selection strategies. The Ruzicka-based feature selection reduces the feature space from 4097 to 1229 features (30% selection ratio) for the 23.6-s Electroencephalogram (EEG) signal while maintaining high classification accuracy. These findings highlight the potential of the proposed approach to improve diagnostic accuracy in medical applications.