In this paper, a new scheme for diagnosis of epileptic seizures in EEG signals using Tunable-Q wavelet transform (TQWT) framework is proposed and benchmarked with Bonn dataset. First, a segmentation of the EEG signals into smaller windows is performed, then a high-pass Butterworth filter with a cutoff frequency of 0.5 Hz is applied to eliminate possible noise. After that, a TQWT with parameters J=8, r=3, Q=1 has been utilized for decomposing the segmented EEG Signals into nine sub-bands. Next, a combination of statistical, entropy-based, and fractal dimension features are extracted from each sub-band. Finally, different ensemble learning-based classifiers, specifically, Adaboost, Gradient Boosting (GB), Hist Gradient Boosting (HistGB), and Random Forest (RF) are employed to classify signals. Also, a feature ranking is driven from classifiers to further analyze the importance of each feature in this particular task. Comparing our method to previous ones, introduced scheme outperforms most of the state-of-the-art works in this field, indicating the effectiveness of the proposed epileptic seizures detection method.

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