## 1 Introduction  
### Background  
Epilepsy is a brain disorder that leads to seizures, which refers to abnormal or excessive neural activities in the brain [1, 2]. Epilepsy is a serious neurological disease that is referred to as a disorder of the central nervous system and is characterized by the loss of consciousness and convulsions [3, 4]. Epilepsy is one of the most known neurological disorders that affect young children and older adults between the ages of 65 and 70 years old. More than 1% of the world's population has been diagnosed with epilepsy [5, 6]. Most epileptic cases can be controlled by anti-epileptic medication or surgery, but a minority of epilepsy patients do not respond to typical therapeutic treatments and surgical interventions [7, 8, 9]. Major causes of epilepsy include head injuries, genetic and infectious illnesses, maldevelopment of the brain, brain tumors, stroke, metabolic problems, and lead poisoning, though, for some patients, the cause of their epilepsy is unknown [7]. According to a survey, at least 65 million people worldwide live with epilepsy, and each year, approximately 2.4 million people are diagnosed with epilepsy, mainly in developing countries;150,000 of these new epileptic patients are in the United States [3]. According to studies, one out of twenty-six people worldwide are affected by epilepsy [10]. Therefore, developing an automated technique that can be implemented on a smart device would be of great importance in detecting and analyzing epileptic seizures as well as other epilepsy-related conditions. There are several anti-epileptic medications on the market that help control the incidence of seizures but cannot cure the disease. In addition, there are side effects associated with these medications, such as tiredness, headaches, reoccurring thoughts, memory loss, and daydreaming. Surgical intervention is another approach for epilepsy treatment; however, there are significant risks involved as well as critical factors to consider, such as being able to identify the focus of the seizure so that the area of the brain involved can be safely removed without resulting in a significant deficit [10]. There are many ways to measure the activity of the human brain, and the methods can be intrusive or non-intrusive. While some techniques study the behavior of the patient, others measure the signals of the eyes, eyelid movements, the face, and even polysomnographic (PSG) recordings. However, epileptic seizure detection has typically been conducted based on physiological recordings, which include electroencephalograms (EEG), electrooculograms (EOG), electromyograms (EMG), and electrocardiograms (ECG) [11; 12]. The recording of seizures and spikes is of primary importance in the evaluation of epileptic patients. Since epileptic seizure detection by PSG requires the patient to attach electrodes to their scalp and remain in the hospital for several hours to record the signals, the process is complex as well as expensive [11]. Considering these challenges, an automatic detection method for epileptic seizures would be of great assistance during the long-term monitoring of epileptic patients. In addition, it could reduce the complexity and improve classification accuracy in epileptic seizure detection. Furthermore, it could also improve the diagnosis and disease management of brain disorders. According to a survey, EEGs are a critical tool for research and diagnosis, especially in detecting epileptic seizures and brain disorder analysis. EEGs are non-invasive and do not involve X-rays, radiation, or injections. EEGs have been used in detecting and analyzing brain signals for many years and are considered safe [6]. The electrodes record EEG signals from the brain without producing any sensation. Minimal side effects include slight redness/skin irritation at the contact site of the electrode, but this typically wears off in a few hours [12]. As a result, EEGs have been widely used as a clinical tool to measure brain activity [13].