## 1 Introduction  
Epilepsy, a chronic neurological disorder that affects the Central Nervous System (CNS), presents a formidable challenge due to its spontaneous recurrent seizures and the absence of a known cure [1, 2]. These seizures not only pose immediate risks such as falls, fractures, and fatalities but also contribute to long-term neurological harm. Symptoms such as disorientation, unusual behaviour, and loss of consciousness often accompany seizures, further compounding the challenges faced by individuals with epilepsy [3]. Detecting impending seizures remains a daunting task due to their unpredictable nature, and most seizures occur without warning. Consequently, researchers have focused on developing methods for predicting seizures, leveraging classification algorithms as a viable approach [1]. Research in epilepsy detection has explored diverse paths, including the application of differential equations to model the dynamic behaviour of the brain's electrical activities [3]. Nonlinear Time Series Analysis (NTSA) has been instrumental in characterizing electroencephalograms (EEGs) based on brain activities [2]. Studies examining EEGs of patients with various conditions, including Parkinson's, depression, Alzheimer's, and epilepsy, have contributed significantly to understanding the complex dynamics of the human brain [4,5]. Epileptic seizures and EEGs of healthy individuals have played crucial roles in categorizing findings in related articles, aiding in identifying and modelling the non-linearity of human brain behaviour [5]. Even minor alterations to the dynamic system parameters of the brain can lead to diverse physiological states, potentially resulting in brain dysfunction or other disorders [5]. Consequently, predicting and understanding epileptic seizures continues to challenge researchers [6]. Improving the quality of life for individuals with epilepsy hinges on accurately detecting and predicting seizures. EEG-based seizure detection remains fundamental in understanding neuronal brain activity [7]. However, the complexity of EEG seizure detection, characterized by dynamic motion and perspective fluctuations, presents a significant challenge [3]. Moreover, the impact of epilepsy on affected individuals is substantial [8]. Seizures, which occur from abnormal electrical discharges of the brain, manifest in various forms, ranging from momentary lapses in attention to severe convulsions [9]. Furthermore, the effects of epilepsy on the CNS result in symptoms such as loss of awareness, unusual behaviour, and confusion, further increasing the risk of injuries [9]. The imperative to predict impending seizures arises from their sudden and unexpected onset [9]. Researchers have grappled with developing methods to anticipate seizures, driven by the potential for physical harm and, in extreme cases, fatality. According to recent data from the World Health Organization (WHO), epilepsy affects approximately 50 million people worldwide, emphasizing the urgent need for effective seizure detection and prediction methods to mitigate its impact [10]. Traditional approaches to seizure detection have relied heavily on visual analysis of EEG recordings by healthcare professionals. However, these methods suffer from subjectivity and time-intensive processes, often leading to delayed responses and missed early seizure indicators [11]. To address these challenges and enhance the efficiency and accuracy of seizure detection, researchers have turned to Machine Learning (ML). ML algorithms offer a promising target for automating seizure detection through EEG data analysis. They excel in processing the vast information contained within EEG recordings and learning to recognize patterns associated with seizures [12]. The integration of ML techniques aims to enable early and precise detection of epileptic seizures to reduce associated risks and minimize false positives [11].