## 1 Introduction
Automatic methods that use ML classification have been investigated for their potential for classification in several healthcare domains, including epilepsy detection, seizure localization, and seizure type identification [25]. There have been several studies of low false alarm detection of epileptic seizure episodes in EEG recordings by ML [4; 8; 9; 14; 18]. Along this vein, various classifiers have been used, such as the k-nearest neighbors algorithm (k-NN) [14], support vector machines (SVM) [15; 26; 27], neural networks (NN) [1; 28], and decision trees (DT) [7]. In [27], for neonatal seizure detection, the authors used 55 features with SVM and reported an overall performance of 89% with only one false seizure detection per hour. The system was trained and tested using EEG data collected from 17 newborns with seizures. The study [29] focused on EEG feature selection by relevance and redundancy analysis and used a back propagation neural network to evaluate the effectiveness of feature selection. Results showed an average seizure detection rate of 91% with a false detection rate of 1.17 per hour. Using the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, the study [30] reported an accuracy of 96% with a false-positive rate of 0.08 per hour. Using the same database, which consists of only 23 subjects, a recent study [31] showed an overall accuracy of 91.8% by deploying linear discriminant analysis as a classifier validated on only five subjects. With the European Epilepsy Database, results achieved include an average sensitivity of 91.72% by neural network classification [32], 90.8% by SVM (false-positive rate of 0.094 per hour) [33], and 93.8% by a feed-forward back propagation artificial neural network [34]. These results all relate to patient-specific experimentation and have not been confirmed on across-patient data. Although there are many studies that focus on seizure detection based on EEG, most of them have some limitations, mainly including experimentation on data from few patientsand using of invasive intracranial EEG records. Furthermore, previous research has used a patient-specific problem formulation and experimental test in which the model is trained and tested on a single patient. Despite attempts to create algorithms to obtain values as accurate as possible, no single one has yet gained widespread acceptance in clinical practice, and the results of studies are far from substituting manual interpretation of the EEG [35; 36]. Indeed, despite the numerous commercially available seizure detection devices, there are several issues that must be taken into consideration before such devices can be used in everyday clinical practice. According to a study published in 2021 [37], a comparative study was conducted between 23 devices on the market for seizure detection/alerting. This study showed that these devices rely largely on movement detectors, autonomic change detectors, heart rates, or eye tracking to detect seizures. According to the study's authors, almost no commercially available seizure detection methods use EEG except for Epihunter, a wearable headband device that is connected to a smartphone and is specifically designed for automatic absence seizure detection, which is a specific type of seizure. Thus, the authors concluded that commercially available seizure detection devices do not have the capability of detecting multiple seizure types. Another recent study [38] compared the sensitivity of three well-known commercial seizure detection softwares (Besa, Encevis, and Persyst) to determine whether they correctly detected seizures over long-term video-electroencephalography monitoring (VEM). Based on results from 81 unseen patients, the researchers found a sensitivity of 67.6% for Besa, 77.8% for Encevis, and 81.6% for Persyst on a patient-by-patient basis. They suggested that the false alarm rate needs to be improved.