# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 5 Discussion
### Research Gaps
While equitable, ethical, and efficient use of shared data remains a concern, data sharing or collaboration with data scientists may provide preclinical researchers with the external expertise to explore the EEG signals and uncover patterns that might not have been originally envisioned. Preclinical investigations, combined with big data analysis of the EEG signals, can lead to swift translational research with the endpoint of producing promising new therapies, alleviating the burden of epilepsy in the general population. A significant burden of epilepsy is the uncertainty of determining an impending seizure [150; 151] in people with refractory seizures [152]. Several lines of research support the idea that seizure generation, or icotogenesis, is not random [153; 154]. Classifying the interictal and preictal discharges is equal to predicting seizure symptoms according to the categorisation of the epileptiform discharges [155]. Prediction of seizures is an emerging area of clinical research which will not only assist the epileptologist in identifying the initiation of seizures but will also help provide early treatment and control of seizures. Seizure prediction has garnered attention with human EEG signal [152; 156; 157; 158; 155] to develop a closed-loop stimulator. A closed-loop stimulator aims to identify the preictaldischarge and stimulate and return the brain to a stable state [159]. The clinical recordings of patients being assessed for epilepsy often last only a few days or a week. The difficulty in capturing spontaneous seizures and long-term continuous electrophysiology has greatly impeded the development of seizure forecasting [160]. Moreover, drug discovery and development is a systematic process that involves several stages before translation into healthcare. One of the stages is preclinical development. This stage aims to understand the mechanism of action of the candidate drugs, assess their potential toxicity, and verify their effectiveness using various laboratory and animal models [161]. Exploring seizure prediction with prolonged EEG recording from rodent models of epilepsy is a feasible and valuable approach to initiate and evaluate new therapeutic approaches that can control and prevent refractory seizures from occurring [162]. Furthermore, preclinical researchers will be provided real-time notification of seizure onset for optimal experimental evaluation in rodents included in the experiments. Figure 5 indicates the lack of research in seizure prediction in rodent models of epilepsy. Rodent models of epilepsy are used to simulate human epilepsy before seizures are detected or predicted. Seizures are first classified as general, focal, or unknown onsets. The classification of seizure or epilepsy type directly affects the selection of optimal treatment [39; 163]. Similarly, the development of ASDs is tailored to a particular seizure. In the recent classification of seizures and epilepsies by ILAE, published in March 2017, both generalised and focal onsets display similar types of seizures; focal onset seizures now encompass seizures (atonic, clonic, tonic, tonic-clonic, myoclonic, and epileptic spasms) that were previously thought to be associated with generalised onset. [PERSON] et al. [115] detected seizures in a clonic seizure dataset, but it is unclear if the seizures are of focal or generalised onset. Developing algorithmic models is the main emphasis of ML/DL seizure detection and prediction. Nevertheless, reporting the onset of seizures in conjunction with the seizure type is essential for identifying studies focusing on the seizure type of a particular seizure onset. In children and adults, focal onset seizures are more prevalent than seizures of generalised onset [164; 165; 166; 167]. Significant correlations exist between focal epilepsy and the frequency of drug-resistant epilepsy [168]. Amidst this prevalence, most studies included in this survey focused on seizure types (absence and generalised tonic-clonic seizures) of generalised seizure onset [103; 105; 106; 107; 108; 109; 110; 111].