Discrimination of Epileptic Events Using EEG Rhythm Decomposition

The use of time series decomposition into sub-bands of frequency to accomplish the oscillation modes in nonstationary signals is proposed. Specifically, EEG signals are decomposed into frequency sub-bands, and the most relevant of them are employed for the detection of epilepsy seizures. Since the computation of oscillation modes is carried out based on Time-Variant Autoregressive model parameters, both approaches for searching an optimal order are studied: estimation over the entire database, and over each database recording. The feature set appraises parametric power spectral density in each frequency band of the Time-Variant Autoregressive models. Developed dimension reduction approach of high dimensional spectral space that is based on principal component analysis searches for frequency bands holding the higher values of relevance, in terms of performed accuracy of detection. Attained outcomes for k-nn classifier over 29 epilepsy patients reach a performed accuracy as high as 95% As a result, the proposed methodology provides a higher performance when is used a optimal order for each signal. The advantage of the proposed methodology is the interpretations that may lead to the data, since each oscillation mode can be associated with one of the eeg rhythms.