# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
###### Abstract  
About 70 million people globally have been diagnosed with epilepsy. Electroencephalogram (EEG) devices are the primary method for identifying and monitoring seizures. The use of EEG expands the preclinical research involving the long-term recording of neuro-activities in rodent models of epilepsy targeted towards the efficient testing of prospective antiseizure medications. Typically, trained epileptologists visually analyse long-term EEG recordings, which is time-consuming and subject to expert variability. Automated epileptiform discharge detection using machine learning or deep learning methods is an effective approach to tackling these challenges. This systematic review examined and summarised the last 30 years of research on detecting epileptiform discharge in rodent models of epilepsy using machine learning and deep learning methods. A comprehensive literature search was conducted on two databases, PubMed and Google Scholar. Following the PRISMA protocol, the 3021 retrieved articles were filtered to 21 based on inclusion and exclusion criteria. An additional article was obtained through the reference list. Hence, 22 articles were selected for critical analysis in this review. These articles revealed the seizure type, features and feature engineering, machine learning and deep learning methods, training methodologies, evaluation metrics so far explored, and models deployed for real-world validation. Although these studies have advanced the field of epilepsy research, the majority of the models are experimental. Further studies are required to fill in the identified gaps and expedite preclinical research in epilepsy, ultimately leading to translational research. epilepsy; electroencephalogram (EEG); epileptiform discharge; seizure detection; seizure prediction; rodent models of epilepsy; machine learning; deep learning +
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recurrence risk (at least 60%) after two unprovoked seizures occurring over the next ten years. (iii) Diagnosis of an epilepsy syndrome [1]. An estimated 70 million people globally have been diagnosed with epilepsy [8], which is a global health burden with adverse social, economic, and psychological impacts on people with epilepsy [9; 10]. The occurrence and frequency of epilepsy vary between countries, with an estimated incidence rate of 49 cases per 100,000 individuals in high-income countries and 139 cases per 100,000 individuals in low- and middle-income countries (WHO, 2023). The estimated prevalence rate of active epilepsy is 6.38 per 1000 individuals [11] and 4 to 10 per 1000 individuals (WHO, 2023). The epilepsy research community has devoted decades of time and resources to studying epilepsy and researching antiseizure drugs (ASDs) in an effort to mitigate the symptoms of epilepsy. Despite the number of ASDs available, one-third of people with epilepsy experience resistance to pharmacotherapy [12], where ASDs fail to control seizures. This group of people bear the major burden of epilepsy in the general population. Thus, a great unmet clinical need exists for identifying and developing new ASDs that efficiently manage pharmaco-resistant spontaneous recurrent seizures (SRSs). The complexity of epilepsy and pharmaco-resistant seizures cannot be thoroughly researched in the clinical setting with humans for ethical reasons [13]. Although cellular models are frequently employed initially to clarify molecular pathways in disease processes, animal models are increasingly useful for preclinical research in studying human diseases and clinical aspects [14; 15] and discovering new drugs and drug targets [16]. In the study of epilepsy and its resistance to ASDs, no singular animal can model the different types of epilepsy [17]. Most animals with central nervous systems are likely to experience epilepsy, and dogs, cats, primates, rats, mice, and zebrafish have all been used to model different types of epilepsy [18]. The selection of a particular animal may be based on practical needs [18].