# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 5 Discussion
### Research Gaps
Furthermore, even with cross validation, the feature subsets exhibit bias towards the modelling algorithm they were assessed by, causing a dependency or overfitting on the modelling algorithm used [91; 178; 179]. It is thus necessary to evaluate the quality of the selected feature subset against another modelling algorithm using an independent validation sample [178]. Embedded feature selection methods integrated into or as an extended functionality of some learning algorithms to perform feature selection during model training should also be explored. Some embedded methods execute feature weighting using regularisation models with objective functions to minimise fitting errors [178]. The embedded methods are computationally efficient compared to wrapper methods; they capture the dependencies between features in the feature set and between features and the target variable [180]. Another feature selection approach, the hybrid method, emerged to address the feature selection inconsistency with several existing feature selection methods. Small changes to training data can significantly impact feature selection outcomes, particularly for small-sized, high-dimensional data [91]. Hybrid feature selection integrates the strengths of other feature selection methods. The feature subset from different feature selection methods provides a comprehensive evaluation of feature importance and increases the probability of discovering the optimal feature subset that is robust to overfitting [181]. A variety of hybrid feature selection algorithms have been proposed: hybrid of genetic algorithm and embedded regularisation [182], mutual information maximisation and an adaptive genetic algorithm [183], two-staged ant colony algorithm [184], binary state transition algorithm and ReliefF algorithm [185], and fuzzy random forest-based feature selection [186]. These hybrid feature selection algorithms and more may be investigated to discover the optimal features for discriminating epileptiform discharges in rodent models of epilepsy. As shown in Table 3, various conventional ML algorithms have been used to identify epileptiform discharges in EEG of rodent models of epilepsy. However, SVM has garnered greater focus than other ML techniques. The ML and DL techniques have been used to model the detection of epileptiform discharges as a strict binary classification task. This task involves distinguishing between different pairs of epileptiform discharges (such as interictal vs. ictal, baseline vs. ictal, and interictal vs. preictal) from fixed-length segmented EEG signals, referred to as epochs. These epochs can either overlap or not. However, a fundamental constraint of this method is the inability of the trained binary classification model to accurately identify samples that contain both partial interictal and ictal events [187]. Typically, binary models trained for epileptiform discharge detection are labelled as seizure-event or nonseizure-event. The trained model would inaccurately classify the crossover sample as either seizure-event or nonseizure-event. Due to the uncertainty associated with crossover samples, rather than assigning EEG segments to a certain class, classifiers based on predicted probability and a threshold to classify the epileptiform discharge event may overcome the uncertainty of dealing with overlapping events. Although some of the ML/DL methods (MLP, GLM, XGBoost) employed in the literature are known probabilistic classifiers, these classifiers, when trained under an appropriate loss function, could model epileptiform discharge detection as a binary probabilistic classification task, eliminating the necessity for postprocessing. Epileptic seizures are infrequent, second- to minute-long episodes that disrupt hours, days, or even weeks of seemingly normal brain activity in the EEG [188]. The EEG dataset is substantially imbalanced due to the wide disparity between nonseizure and seizure events [189; 190]. Generally, class imbalance is a challenge in biomedical signals [191].