## 1 Introduction  
Epilepsy, a common chronic brain disease, is caused by sudden excessive discharge of brain nerve cells. Nearly 1% of the world's population is suffering from epilepsy. During seizures, patients have transient involuntary convulsions in one part of the body (partial seizures) or the entire body (generalized seizures), sometimes accompanied by a loss of consciousness and urinary and fecal incontinence [1], greatly affecting the patients' life quality. The electroencephalogram (EEG) is a representative signal containing information on brain electrical activity, which is used as a tool for clinical diagnosis and analysis of epilepsy [2]. Since the 1970s, researchers have carried out a lot of research on seizure detection and prediction tasks [3; 4; 5]. Automatic epilepsy detection technology can help doctors improve the accuracy of epilepsy diagnosis, greatly save time, enhance the efficiency of diagnosis, and strive for more rescue time. Seizure detection comprises mainly two parts: feature extraction and classification of EEG signals. In terms of feature extraction, this can be roughly divided into linear analysis methods and nonlinear analysis methods. Linear analysis methods mainly include time-domain analysis, frequency-domain analysis, and time-frequency domain analysis. Early EEG analysis directly extracted features from time-series signals, including time-domain features such as peak value, rhythm, duration, and sharpness [3]. After that, researchers also extracted such characteristics as spike rhythm, the relative amplitude of the EEG signal [4], fractional linear prediction error energy [6], and line length [7] to classify EEG signals. Although the time-domain waveform contains all EEG signals, this method lacks objectivity with large errors. Studies in the literature [8; 9] transformed the original EEG signal from time-domain to frequency-domain and extracted the corresponding spectral components for frequency domain analysis. The frequency domain is very important when dealing with epilepsy. In medicine, the brainwaves can be divided into five main bands in the frequency domain, namely, delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-80 Hz). After analyzing the above medical frequency bands, [PERSON] et al. [10] further divided the beta band into four sub-bands (beta-1, 12-15 Hz; beta-2, 15-18 Hz; beta-3, 18-25 Hz; and hi-beta, 25-30 Hz), which had a positive effect on the analysis of EEG signals. Recent studies have shown that the sub-bands are also useful in the classification of epileptic EEG signals. [PERSON] et al. [11] first proposed the systematic evaluation of frequency sub-bands for the classification accuracy of epileptic EEG. The results showed that additional frequency band analysis was conducive to the detection of epileptic EEG. However, EEG signals are non-stationary and time-varying, and the premise of frequency domain analysis is that the stationary random signals and the features extracted in the frequency domain do not contain time information. Therefore, a time-frequency analysis method combining the time domain and frequency domain has been gradually developed. [PERSON] et al. [12] used short-time Fourier transform (STFT) to extract time-frequency information from EEG signals and automatically generate optimized features for each patient. In addition, wavelet transform [13; 14; 15; 16] is also an effective tool for time-frequency feature extraction of EEG signals. Studies show that brain activity has complex dynamic characteristics, so it can be regarded as a nonlinear dynamic system. More and more researchers have become interested in nonlinear analysis methods for EEG signals. [PERSON] et al. [17] used a multiscale complexity measure to extract the nonlinear feature of the scale-related Lyapunov exponent for classification. [PERSON] [18] proposed a novel EEG feature extraction approach for determining a correlation dimension to analyze the nonlinear characteristics of epileptic EEG signals.