# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 5 Discussion
### Research Gaps
Furthermore, epilepsy and refractory epilepsy affect people of all ages, with a high incidence and prevalence in children compared to adults/mixed age studies [168; 169; 170]. Although the incidence of epilepsy in children declines as they age, epilepsy in children is highest in the first year of life [167]. Thus, it negatively impacts their quality of life and the family's functionality [171]. Absence seizures usually begin in childhood, between the ages of four and ten, even though it has been documented in individuals of all ages [172]. Detection of absence seizures has been extensively studied [103; 105; 106; 107; 108; 109; 110; 111], in addition to few studies on [PERSON] syndrome. No study has aimed to detect or predict seizures in neonatal (benign familial neonatal epilepsy) and other epilepsy syndrome during infancy ([PERSON] syndrome), childhood ([PERSON] syndrome, Landau-Kleffner syndrome), and adolescence (juvenile myoclonic epilepsy). Linear features have been widely applied in detecting seizures in rodent models of epilepsy [103; 106; 107; 108; 109; 110; 111; 112; 115; 116; 117; 118; 119; 120]. Despite the good performances obtained from using these features, linear features cannot identify the EEG changes that preceded seizures because the brain is a nonlinear dynamic system [90]. Given the nonlinear complexity of the brain, it is suitable to analyse the changes in activities of the brain with nonlinear features [173]. Nonlinear energy [111] and approximate entropy [107; 108; 101] have been used together with linear features. However, the approximate entropy values for the ictal and interictal signals overlap; hence, approximate entropy has to be combined with a complementary feature [107]. The dynamism of the brain signal originates from the change in statistical and frequency components over time [174], hence the dynamical property of the EEG signal [173]. Previous studies have focused on extracting features from the time and frequency domains. The time and frequency domains capture the amplitude over time and the frequency components, respectively. These two domains fail to capture the dynamicity of the EEG signal frequencies [174]. Time-frequency domain analysis has been shown to be an effective tool for studying dynamical signals since the time and frequency information is integrated into a single representation [175]. The benefits of feature engineering include enhanced model generalisation, reduced memory usage, increased computing efficiency, and improved learning performance [91]. Hence, feature selection and transformation are recognised as effective dimensionality reduction approaches. From Table 2, four studies reduced feature dimension using feature transformation techniques [102; 107; 118; 120]. This dimension reduction approach captures the underlying patterns and relationships within the dataset, improving the ML models' performance. However, the loss of some of the original information in the feature set and the inability to interpret the linear combination of the original features [176] lead to a lack of optimal discriminative features for interpretative classification, but they are suitable for exploratory data analysis [177]. On the contrary, feature selection involves selecting a small subset of features from the original feature set, thus preserving the interpretation and leaving the original feature set unchanged [178]. Four studies in this survey [103; 109; 112; 115] explored feature selection. The forward feature selection, a wrapper-based feature selection, uses a search algorithm to iteratively determine the optimal feature to include in the set of selected features; a modelling algorithm acts as a black box to assess the quality of the feature subset. However, with a high-dimensional feature, where d is the number of features, the search space grows by \(2^{d}\), leading to computational intensity in finding the optimal subsets of features [91].