A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection
Wavelet decomposition of epileptic EEG into sub-band signals helps to exhibit significant distinction between interictal and ictal EEG states. A framework on wavelet-based nonlinear features and extreme machine learning is proposed for the seizure detection. The union of sub-band features leads to better identification of seizure activities. Three nonlinear methods attain better detection performance to that of classic wavelet-based energy analysis using the same classifier. The proposed ELM detector achieves not only a high detection accuracy but also a very fast learning speed.
seizure detection wavelet decomposition approximate entropy (apen) sample entropy (sampen) recurrence quantification analysis (rqa) extreme learning machine (elm) support vector machine (svm)