Key result
A pattern recognition strategy using discrete wavelet transform, linear discriminant analysis, and a K-Nearest Neighbour classifier achieved 100% accuracy in classifying epileptic seizure EEG signals.
Why the study?
Does a wavelet-based feature extraction strategy with PCA/LDA and Naïve Bayes/K-NN classifiers accurately classify normal and epileptic seizure EEG signals?
Population
EEG data (normal and epileptic seizure signals)
Design
Other
Authors
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May aid automated EEG seizure detection; leaves open clinical validation in prospective cohorts.
Does a wavelet-based feature extraction strategy with PCA/LDA and Naïve Bayes/K-NN classifiers accurately classify normal and epileptic seizure EEG signals?
A pattern recognition strategy using discrete wavelet transform, linear discriminant analysis, and K-Nearest Neighbour classification can achieve up to 100% accuracy in detecting epileptic seizures from EEG signals.
Sharmila et al. (2017) studied Epileptic seizure. Discrete wavelet transform (DWT) with PCA/LDA and Naïve Bayes/K-NN classifiers was evaluated on Classification accuracy of normal vs epileptic seizure EEG signals. A pattern recognition strategy using discrete wavelet transform, linear discriminant analysis, and a K-Nearest Neighbour classifier achieved 100% accuracy in classifying epileptic seizure EEG signals.
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