## 3 Related Work  
Several researchers have proposed techniques for seizure detection through EEG signal analysis. Such studies span advancing ML techniques. This section categorizes the reviewed papers into specific themes: early detection, feature engineering, classification  
Figure 1: Types of seizures and subtypes. techniques, and comprehensive reviews and methodologies. Several researchers proposed different techniques for early seizure detection. [PERSON] [13] introduced a patient-specific seizure onset detection method leveraging Support Vector Machine (SVM) analysis of scalp EEG signals, achieving an accuracy of 96%. Similarly, [PERSON] et al. [14] presented a preital state detection model with promising accuracy but lacked information on false alarm rates, critical for assessing the model's reliability in real-world settings. [PERSON] et al. [15] also presented an early seizure detection algorithm for implantable closed-loop stimulation devices, achieving high sensitivity with minimal detection delay. However, specific dataset details and implementation challenges were lacking. Feature extraction and selection play a crucial role in enhancing the performance of seizure detection models. [PERSON] and [PERSON] [16] proposed an automated method integrating frequency and time domain features with a flexible wavelet selection algorithm, achieving 97.08% accuracy using Random Forest classifiers. However, their work lacked significant details regarding computational efficiency and real-time feasibility. [PERSON] and [PERSON] [17] examined feature selection and classification techniques for epileptic seizure prediction, emphasizing the trade-off between accuracy and computational time with the PCA algorithm. While achieving high accuracy without PCA, they noted the need for further optimization to balance accuracy and computational efficiency. [PERSON] and [PERSON] [18] proposed two deep learning models for patient-specific seizure detection using CHB-MIT data: a 1D CNN and a CNN-LSTM hybrid. The models achieved 94.83% accuracy, 90.18% sensitivity, and 99.48% specificity. Despite improvements, the variability in performance across different patients highlights challenges in developing a generalized detection system. [PERSON] et al. [19] proposed an approach for detecting epileptic seizures in EEG signals using the line length feature extracted through DWT and a three-layer MLPNN for classification. Despite high classification accuracies of up to 99.60%, limitations regarding dataset preprocessing were acknowledged, necessitating further validation under real clinical conditions. On the other hand, [PERSON] and [PERSON] [20] introduced Permutation Entropy (PE) and SVM for automated epileptic seizure detection, achieving high accuracy with a reported sensitivity of 94.38% and specificity of 93.23%. However, the focus on post-event classification without real-time detection challenges posed limitations. Classification techniques form the backbone of seizure detection models. [PERSON] et al. [21] proposed a postictal seizure detection method with impressive sensitivity and specificity, achieving 100% sensitivity and 98.5% specificity. However, its exclusive focus on postictal detection posed limitations in early intervention scenarios. Similarly, [PERSON] and [PERSON] [7] proposed a framework for detecting epileptic seizures using DWT coupled with Naive Bayes and KNN classifiers, achieving remarkable accuracy in classifying various epileptic dataset subtypes, with reported accuracies of over 99%. However, the study lacked exploration of enhancing overall seizure detection or tracking treatment effectiveness. Further, [PERSON] et al. [22] introduced a hybrid EEG classification approach using Grey Wolf Optimizer and SVMs, achieving an accuracy of 99%. However, specific accuracy and sensitivity metrics were lacking, hindering a comprehensive evaluation of the model's performance.