Key result
The WBCKNN classifier achieved a maximum classification accuracy of 99.67% for two-class and 99.0% for three-class epilepsy detection problems, outperforming traditional machine learning models.
Why the study?
EEG signals reflect seizures and epilepsy, prompting the development of an automatic detection classifier that overcomes neighborhood size sensitivity and provides robust classification.
Does the WBCKNN classifier improve the accuracy of epilepsy detection from EEG signals compared to traditional classifiers?
Does the WBCKNN classifier improve the accuracy of epilepsy detection from EEG signals compared to traditional classifiers?
The proposed WBCKNN classifier with Fourier transform preprocessing achieves high accuracy in detecting epileptic seizures from EEG signals, outperforming traditional machine learning models.
May support automated EEG epilepsy detection; hypothesis-generating and requires prospective clinical validation before use.
The electroencephalogram (EEG) signals are important for reflecting seizures and the diagnosis of epilepsy. In this paper, a weighted k-nearest neighbor classifier based on Bray Curtis distance (WBCKNN) is proposed to implement automatic detection of epilepsy. The Fourier transform can transform the time-domain characteristics of the signal into frequency domain, which can display more useful information. The WBCKNN classifier can well overcome the sensitivity of the neighborhood size k and has good robustness. Therefore, it can classify EEG signals more accurately for different situations. WBCKNN is applied on public dataset and tested by k-fold cross-validation. Experimental results show that the best accuracy of the two-classification problems and three-classification problems is 99.67% and 99%, respectively. Compared to other classifiers, the accuracy of classification is also improved. In addition, this method is superior to traditional methods in sensitivity, specificity and false alarm rate of epilepsy classification. This method can be applied to the medical market to help doctors diagnose epilepsy.
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Wang et al. (2020) studied Epilepsy (n=500). WBCKNN classifier vs. Traditional classifiers (kNN, SVM, DT, RF, XG-boost, DNN) was evaluated on Classification accuracy. The WBCKNN classifier achieved a maximum classification accuracy of 99.67% for two-class and 99.0% for three-class epilepsy detection problems, outperforming traditional machine learning models.
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