# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 5 Discussion  
### Research Gaps  
The increasing accessibility of computational resources, along with the limitations of manual and thresholding methods in detecting epileptiform discharges, contributes to the advancement of ML and DL techniques in this field. Most attention has been directed towards applying ML and DL to analyse human EEG seizure signals for healthcare delivery. Various categories of ML/DL techniques have been exploited. Considering  
Figure 6: Number of eligible articles using various performance evaluation metrics in previous studies. recent studies, feature-based ML algorithms include random forest [124; 125; 126; 127], gradient boosting [125], extra tree [125], SVM [124; 128; 129; 130; 131], naive Bayes [125], XGBoost [127], linear regression [129], and k-nearest neighbour [129]. Deep learning techniques include artificial neural network [129], long short-term memory (LSTM) [129; 132], CNN [132; 133; 134; 135; 136; 137; 138], modified-CNN [138], tabNet [127], recurrent neural network (RNN) [139], and temporal convolution network [137]. More recent is the use of hybrid models, leveraging the strength of more than one algorithm to overcome individual limitations and build a robust model; these include CNN-LSTM [132; 137], RNN-LSTM [133; 135], CNN-SVM [138; 140], simple CNN with modified-CNN [138], and LSTM with multi-scale atrous-based-DCNN [141]. Due to impending preictal and interictal class imbalance, [PERSON] et al. [137] developed unsupervised deep learning seizure models for predicting seizures. These models, known as anomaly detectors, are based on CNN autoencoder, CNN-LSTM autoencoder, and TCN autoencoder trained on interictal discharge for detecting preictal discharge as an anomaly. These models as applied to human EEG have demonstrated impressive performance on the test sets. However, models developed with human EEG signals obtained through noninvasive EEG, which typically have a range of channels from a single channel to 64 or 128, fail to generalise rodent EEG signals obtained from invasive EEG with at most 2 channels. Most articles on rodent models for seizure detection have used SVM compared to other algorithms applied in a single study [107; 110]. However, since 2019, impressive performance has been achieved by applying complex algorithms such as XGBoost, MLP, and GNN (see Table 3). Interestingly, XGBoost is also used for seizure detection in human EEGs. Recently, DL has been investigated for seizure detection in rodent models of epilepsy, such as CNN (see Table 4). CNN methods are also commonly used for seizure detection in human EEGs. However, the utilisation of hybridised models in rodent models of epilepsy has not yet been investigated. Advancing ML and DL methods in identifying epileptiform activity in rodent models of epilepsy provides notable benefits, such as automated detection of epileptiform patterns with improved performance, early prediction of seizures, effective management of extensive datasets, and support for longitudinal studies. A growing number of articles have not been published regarding the detection and prediction of seizures in rodent models of epilepsy, as shown in Figure 4. This may be due to data restrictions or a lack of access to the rodent EEG signal repository. On the contrary, the seizure detection and prediction algorithms developed by research during the last five years, based on human EEG data, have mostly depended on publicly accessible datasets (University of Bonn Database [142], CHB-MIT Scalp EEG [143; 144], Melbourne-NeuroVista seizure trial [145], Kaggle UPenn and Mayo Clinic's Seizure Detection Challenge, TUH EEG Seizure Corpus [146], Helsinki University Hospital EEG Database [147; 148]). Although an essential precaution for patient safety is the use of rodents in research, owing to the confidential nature of the research that produces the rodent EEG data, preclinical EEG data sharing is not yet a common practice [149].