## 1 Introduction  
The demand has been increasing for implementing fast automatic epileptic seizure detection, shortening healing time, raising the success rate of cure, protecting patients from accidents, and saving their lives. As such, automatic epileptic seizure detection has become a hot research topic [1; 2; 3; 4; 5]. Recently, an incident report of epilepsy, one of the most common neurological disorders, stated that the prevalence is about 1 to 2% among the global population, and is rapidly increasing in the elderly population [1; 4; 5; 6]. In the past several decades, although new anti-epileptic drugs had been invented, a third of epileptic patients are still suffering from epileptic seizures. Additionally, even though seizures are treated and controlled, self-reported life quality is significantly reduced by the unpredictability and consequences of epilepsy, as the disease causes many anomalies in terms of behavior, awareness, movement, and sensation [3; 7; 8]. The significant differences between epileptic seizure and healthy states are generally highlighted as the frequency and patterns of neurons, meaning the spatial-temporal patterns of neurons gradually increase from the normal state to epileptic seizure-free state and then to the epileptic seizure state [7; 9]. The World Health Organization (WHO) stipulates that epilepsy is caused by a group of brain cells with unexpected, uncontrolled electrical discharges, termed 'epileptic seizures' [6; 9; 10]. In 1929, [PERSON] first measured the spontaneous electrical activity in the brain using electrodes, with electroencephalographic (EEG) signals being produced. His method is still considered a commonly used technology to monitor activity in the human brain. Afterwards, it was further used to detect epileptic seizure [9; 10; 11; 12; 13; 14]. In the past and at present, well-trained and experienced neurophysiologists are employed universally to detect epileptic seizures through conventional methods by inspecting EEG signals over a long period [6; 9; 10; 13; 14; 15; 16]. Practically, to ensure high accuracy, detailed evaluations are essential, such as brain imaging tests like magnetic resonance imaging (MRI), computed tomography (CT), long-term EEG tests, blood tests, and medical history examination [17; 18; 19; 20; 21; 22; 23; 24]. All of these evaluations are costly, which places a heavy financial burden on patients, especially those living in areas with frequent epileptic seizure breakout and poverty. Unfortunately, these methods have many limitations: for example, they are time-consuming, tedious, subjective, [9; 10; 13; 14; 15; 16; 25], and face many challenges in terms of inevitable factors on reducing misjudgments [13]. Computer-aided detection with EEG signals can therefore be considered a promising technology. It can be used to realize the goal of minimizing epileptic seizure activity to help control the disease [16; 26]. Recently, the signal-processing-based method has been applied to extract features to analyze, recognize, and detect epileptic seizures [1; 2; 3; 4; 5]. Between 2014 and 2017, many different methods were being used to extract and recognize features to achieve the automatic detection of epileptic seizures, such as transform techniques (e.g., discrete short-time Fourier transform, wavelet transform, generalized S-transform, shearlet and contourlet transforms), nonlinear dynamic analyses (e.g., various entropies and the Lyapunov exponent), and data-dependent time-frequency decomposition approaches (e.g., empirical mode decomposition and local mean decomposition) [5; 7; 15; 17; 18; 19; 20; 21; 22; 23; 24; 26; 27; 28; 29; 30; 31; 32]. However, although numerous methods have been effectively used to achieve the automatic detection of epileptic seizures, there are still some problems affecting improved recognition accuracy. This fact illustrates the nonlinear and non-stationary problem in EEG signals, which causes many challenges in analyzing the signals with conventional methods [30; 31; 33].