## 2 Related Work  
The application of machine learning-based techniques for the classification and identification of various medical conditions has garnered significant interest among researchers [5; 6; 7]. One specific area that has gained substantial attention is the use of these techniques for the identification and classification of epileptic seizures. Table 1 provides a summary of some of the machine learning methods used in the detection and classification of epileptic seizures, which include support vector machine (SVM), convolutional neural networks (CNN), and extreme learning machines (ELM). The authors in [8] utilized deep CNN to develop a model to detect mental fatigue from electroencephalography (EEG) data. The proposed model was trained on a dataset containing EEG data labeled as fatigued or rested from 20 participants. The model achieved an accuracy of 97% to classify the mental state of the participants. Epileptic seizures are categorized by the International League Against Epilepsy (ILEA) into two main types: focal seizures and generalized seizures. A focal seizure, also known as partial seizure, affects only a portion of the cerebral hemisphere [9]. The level of awareness further separates focal seizures into two categories: aware (simple-partial) and impaired awareness (complex-partial). A seizure in which consciousness is intact is categorized as a focal-aware seizure. A seizure event where a person behaves abnormally, like chewing or mumbling, and is not aware of the surroundings is classified as a focal seizure with impaired awareness. The types of epileptic seizures as defined by ILAE [10] are shown in Figure 1.  
[PERSON] et al. [11] used a variable window approach for the segmentation of EEG data by placing boundaries where there is a sharp change in the energy of the signal. The authors propose a multi-task approach for seizure detection to address the challenge of  
Figure 1: Types of epileptic seizures. inter-patient and intra-patient variability in the characteristic patterns found in seizure signals. The authors achieved a reduction in the false positive rate (FPR) of more than 10% in 15 cases with the proposed method. In [12], a covariance matrix was employed to analyze EEG recordings of epileptic patients and to reduce the dimensionality of the signal before feature extraction. An accuracy of more than 99% was achieved by employing non-parametric tests to find the set with the most distinctive characteristics and subsequently fed into the adaptive boosting least square-support vector machines (AB-LS-SVM) for classification. [PERSON] et al. identified non-linear characteristics from EEG data and used the extracted features as input into a decision tree (DT) for epilepsy classification [13]. Following the feature extraction phase, the data samples were divided into two groups for training and testing. Finally, the performance was evaluated using parameters such as sensitivity, specificity, F1-score, and accuracy. [PERSON] et al. [14] proposed a model for epilepsy detection using a hybrid feature set that integrates temporal, spectral, and time-spectral features. The authors classified epilepsy data using Gaussian mixture models. The proposed approach achieved an accuracy of 86.26%. [PERSON] et al. [15] demonstrated that 100% sensitivity with zero false positives can be achieved by using four different classification algorithms with DWT for signal decomposition in the preprocessing phase. The classification models were trained and tested using the data from the same subjects. [PERSON] et al. proposed a seizure detection model based on random forest that can be employed in an embedded closed-loop device. The model detected seizures with 93.84% sensitivity [16]. [PERSON] et al. [17] employed SVM and other neural networks for classifying epileptic events using the publicly accessible BONN dataset, including multi-layer perceptrons, long short-term memory networks (LSTM), and probabilistic neural networks. The efficiency of each classification model was evaluated using a majority vote technique.