## 3 Related Work
[PERSON] and [PERSON] [23] introduced a novel approach for automated epileptic seizure detection using sample entropy for feature extraction and an extreme ML for classification, emphasizing high accuracy and computational speed. However, specific dataset details were not specified. [PERSON] et al. [24] provided a comprehensive review of automated EEG signal classification methodologies, emphasizing non-linear features like entropy. While achieving notable classification accuracy, the research gap lies in the necessity for more diverse data and refined features to enhance accuracy. Similarly, [PERSON] et al. [25] conducted a systematic literature review examining ML techniques for epileptic seizure detection, focusing on various feature selection and classification techniques. Their taxonomy of state-of-the-art solutions provides insights into challenges and opportunities in the field, yet gaps in specific methodology details were noted. [PERSON] et al. [26] discussed ML-based classification models for epileptic EEG signals, comparing RF, DT, and Extra Tree classifiers. Despite achieving high accuracy, explicit limitations of the work were not mentioned. In addition, [PERSON] and [PERSON] [27] proposed an ML approach for constructing patient-specific classifiers for detecting epileptic seizures using scalp EEG data, showcasing accurate detection with a 96% detection rate. However, the reliance on patient-specific classifiers posed limitations. [PERSON] and [PERSON] [28] presented a patient-dependent offline system for seizure detection in epilepsy diagnosis, achieving high specificity, sensitivity, and low false-positive rates. However, specific dataset details and limitations were not disclosed. [PERSON] et al. [29] also proposed an approach for detecting epileptic seizures in EEG signals achieving a 96.25% detection accuracy. The study highlights the potential of deep learning models in improving seizure detection but notes limitations related to generalizability across different patient profiles. [PERSON] et al. [30] proposed deep-learning models for predicting epileptic seizures using iEEG signals. They developed a CNN and a CNN-LSTM hybrid model, achieving a maximum accuracy of 95.48%, sensitivity of 92.37%, and specificity of 96.18%. While the models demonstrated strong performance, challenges remain in handling variations in seizure patterns across different patients. In addition, [PERSON] et al. [31] proposed a patient-specific seizure detection method using phase space representation and time-delay embedding to analyze EEG dynamics. PCA was applied for dimensionality reduction, and key features were classified using LDA and Naive Bayesian classifiers. The results achieved 88.27% sensitivity and 93.21% specificity. However, its reliance on patient-specific models may limit broader applicability. Also, [PERSON] and [PERSON] [32] proposed an autonomous epilepsy detection system using a Time-Frequency (TF) entropy measure to reduce EEG analysis time. The method computes the TF spectrum via S-transform and extracts entropy features, classified using an LSSVM classifier. The results achieved 86% accuracy with an AUC of 0.914. However, computational efficiency remains a consideration for real-time applications. Moreover, [PERSON] et al. [33] proposed a framework for epileptic focus localization using DWT and SVM. The method optimizes DWT parameters by analyzing seven wavelet families and selecting the best decomposition levels for feature extraction. The results achieved 83.07% accuracy. Also, [PERSON] et al. [34] proposed an automatic seizure detection system for newborns, focusing on feature selection via relevance and redundancy analysis. Using correlation-based and Relieff methods, key EEG features were ranked and optimized for classification with a backpropagation neural network. The results achieved a 93% overall detection rate with a false seizure detection rate of 1.17/h. The approach enhances neonatal seizure detection by accounting for age-specific EEG characteristics. [PERSON] et al.