Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection

In this paper, a comprehensive method using symmetric normal inverse Gaussian (NIG) parameters of the sub-bands of EEG signals calculated in the dual-tree complex wavelet transformation domain is proposed for classifying EEG data. The suitability of the NIG probability distribution function is illustrated using statistical measures. A support vector machine is employed as the classifier of the EEG signals, wherein the NIG parameters are used as features. The performance of the proposed method is studied using a publicly available benchmark EEG database for various classification cases that include healthy, inter-ictal (seizure-free interval) and ictal (seizure), non-seizure and seizure, healthy and seizure, and inter-ictal and ictal, and compared with that of several recent methods. It is shown that in almost all the cases, the proposed method can provide 100 % accuracy with 100 % sensitivity and 100 % specificity while being faster as compared to the time-frequency analysis-based and EMD techniques.