## 2 Related Work
The results demonstrated that SVM outperformed the other machine learning models in terms of accuracy. [PERSON] et al. [18] utilized a five-level decomposition of EEG signal using empirical wavelet transform along with kurtosis-based channel selection to identify the electrodes containing the most relevant information to improve the performance of seizure detection. The proposed algorithm achieved mean sensitivity and specificity of 99.77% and 99.88%, respectively. [PERSON] et al. proposed a deep CNN-based model with 13 layers to analyze the EEG signal. The authors used raw EEG signals instead of feature extraction and selection for the classification of pre-ictal, normal, and seizure classes. The model achieved an accuracy of 88.67%, as reported in [19]. [PERSON] et al. [20] performed a classification of EEG data using ANN and K-nearest neighbor after extracting statistical features such as Higuchi fractal dimension and expected activity measurement from the signal. The findings demonstrate that ANN and k-NN both attained accuracy rates of 94% and 98%, respectively. [PERSON] et al. [21] used multiple ML techniques such as random forest, SVM, KNN, and Adaboost to perform localization of epileptic seizures by classifying EEG data containing EEG signals of focal epilepsy using various statistical features. The results demonstrate that SVM has achieved the highest classification accuracy of 96.1%. [PERSON] et al. [22] employed DWT to develop a method to find the optimal DWT parameters by breaking down the EEG signal into seven commonly used mother wavelets to detect epileptic seizures with improved accuracy and a lower cost of computation. Various DWT mother wavelets such Bior6.8, coif3, db10, and haar were used to decompose the data. The highest performance in terms of accuracy of 92.30% was achieved using a coif3 mother wavelet. [PERSON] et al. [23] proposed a seizure detection technique utilizing only two bipolar channels from the CHB-MIT dataset. DWT was used to decompose the EEG signal prior to feature extraction. The proposed method detected seizures with sensitivity and specificity of 96.6% and 92.5%, respectively. The identification and localization of temporal lobe epilepsy have been another topic of interest in machine learning-based epilepsy diagnosis [26]. Seizure localization refers to the process of identifying the brain region where a seizure originates. Localized seizures that occur in either the right or left hemisphere of the brain are typically curable with surgery. Certain types of seizures may be treated with anti-epileptic medicines; however, in some cases, people with partial seizures may require surgery [27]. Therefore, finding the location of a seizure is crucial for the treatment of epilepsy [28; 29]. [PERSON] et al. used two algorithms based on decision forest i.e., SysFor and Forst CERN, to identify the location most affected by seizure [30; 31; 32]. [PERSON] et al. [25] proposed three different methods to perform binary classification of epilepsy data containing focal and non-focal signals using the BONN dataset to detect epileptogenic zones. The proposed method that combines deep-CNN with wavelet synchrosqueezing transform for feature extraction demonstrated the highest accuracy of 99.7% as compared with hybrid-SVM and 1D-CNN, which achieved accuracies of 99.3% and 99.4% respectively. In this research, we studied EEG analysis and machine learning techniques for epilepsy detection. It is evident from the literature that there is increasing interest among researchers in this topic and that significant progress is being made in epilepsy detection. However, there are still significant challenges that, if addressed, will help advance the research further. Imbalanced data are a common issue with epilepsy data. If not properly addressed, the results may be inaccurate. Therefore, more work needs to be carried out on accurate epilepsy detection considering imbalanced datasets where the normal class is hours long and epileptic events are a few seconds/minutes.