An empirical wavelet transform based Hilbert marginal spectrum combined with a random forest classifier achieved 99.3% accuracy in classifying epileptic seizure EEG signals with a 50% training rate.
Does the proposed EWT based HMS method improve the classification accuracy of epileptic seizure EEG signals?
The proposed EWT based HMS method with a random forest classifier achieves high accuracy in classifying epileptic seizure EEG signals.
This paper proposes a new method for the classification of epileptic seizure electroencephalogram (EEG) signals. Empirical wavelet transform (EWT) based Hilbert marginal spectrum (HMS) has been derived. In order to segment the Fourier spectrum of the EEG signals, the scale-space representation based boundary detection method has been employed. Then, EWT is used to decompose EEG signals into narrow sub-band signals and HMS of these sub-band signals have been computed. For a synthetically generated multi-component frequency modulated signal, the EWT based HMS is compared with the conventional Fourier spectrum obtained using fast Fourier transform (FFT) algorithm. Three features have been extracted from these HMSs which belong to distinct oscillatory levels of the EEG signals and probability (p) value based feature ranking is performed. Finally, the selected features are fed to random forest classifier for classifying EEG signals of seizure and seizure-free classes. We have achieved 99.3% classification accuracy with only 50% training rate which shows the usefulness of the proposed method for the classification of epileptic seizure EEG signals.
Bhattacharyya et al. (Tue,) conducted a other in Epileptic seizure. Empirical wavelet transform (EWT) based Hilbert marginal spectrum (HMS) and random forest classifier vs. Conventional Fourier spectrum obtained using fast Fourier transform (FFT) was evaluated on Classification accuracy of seizure and seizure-free EEG signals. An empirical wavelet transform based Hilbert marginal spectrum combined with a random forest classifier achieved 99.3% accuracy in classifying epileptic seizure EEG signals with a 50% training rate.