# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 4 Results
### ml and ml Article Distributions
Three out of the total number of articles reviewed considered at least one nonlinear feature; [107; 108] measured complexity with approximate entropy while [111] included nonlinear energy. Forward feature selection otherwise known as sequential forward selection, a type of sequential feature selection, was employed by three studies [103; 109; 115]. Three studies transformed features into a low-dimensional feature space using PCA [102; 107; 120]. In addition to using PCA, [PERSON] et al. [120] used a Laplacian eigenmap manifold. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref** & **Rodent Model** & **Seizure/Epilepsy Type Modelled** \\ \hline
[110] & \(\gamma\)-aminobutyric acid A (GABAA) receptor mutation (\(\gamma\)2R43Q) & Absence epilepsy \\
[102] & Intrahippocampal kainic acid & Mesial temporal lobe epilepsy \\
[112] & High temperature & Dravet syndrome \\
[109] & Genetic and kainate systemic injection & Absence and tonic–clonic seizures \\
[103] & Genetic and kainate systemic injection & Absence and limbic seizures \\
[108] & Genetic & Absence seizures \\
[107] & Genetic & Absence seizures \\
[120] & - & - \\
[34] & Intra-amygdala kainic acid, Dravet, Pilocarpine & Temporal lope epilepsy, Dravet syndrome \\
[115] & Pentylenetetrazole & Clonic seizures \\
[106] & GAER & Absence seizures \\
[104] & Perforant pathway stimulation & Mesial temporal lobe epilepsy with \\  & & hippocampal sclerosis \\
[114] & Pilocarpine hydrochloride & Convulsive seizures \\
[113] & Pilocarpine injection & Generalized tonic–clonic seizures \\
[105] & GAERS & Absence seizures \\
[117] & Perforant path kindling susceptible & Post-traumatic epilepsy \\
[116] & Kindling & Frontal lobe epilepsy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of rodent models and seizure types analysed in recent studies. RQ3: What ML or DL Methods Have Ben Exploited in Detecting and Predicting Seizures from EEG of Rodent Models of Epilepsy? The thresholding technique is the earliest approach to the automatic detection of epileptiform discharge [121]. In this approach, a feature is computed for each consecutive window of an EEG signal, and the seizure segment is established by comparing the extracted feature with a defined threshold [122]. However, the lack of a gold standard threshold is a drawback as this approach highly depends on the value and type of threshold defined [34]. The benefit of machine learning over the threshold approach lies in its ability to learn and discover patterns inherent in the signal. Consequently, [PERSON] et al. [111] applied a support vector machine (SVM) and backpropagation neural network in differentiating the baseline signals from the seizure segments in the EEG of five rats. The SVM model outperformed the neural network model with an accuracy of 100%. However, the baseline and the seizure classes were highly imbalanced, with 38 epochs of baseline and 152 epochs of seizures. [PERSON] et al. [118] investigated the performance of WLLE and LLE in combination with SVM in 16 male Swiss mice. WLLE separated the data into clusters, whereas LLE clusters overlapped. WLLE and SVM performed with an accuracy of 72.79-90.66% and a false-alarm rate of 0.00%. [PERSON] et al. [100] assessed the performance of five- and ten-fold cross-validation in discriminating interictal and ictal spikes in five rats. The model trained with ten-fold cross-validation was evaluated to a considerable sensitivity of 93.03% and specificity of 94.97%. However, the lack of evaluation of the model with an independent test set and the imbalance of the 3106 interictal and 2356 ictal class imbalances places a limitation. To determine the type of SVM suitable for identifying ictal data, [PERSON] et al. [119] proposed using support vector data description (SVDD) in identifying interictal and ictal spikes. The model proffered 100% sensitivity and 79-85% specificity after training with two-fold cross-validation. [PERSON] et al. [101] developed a linear discriminant-based model with the EEG signal from 11 rats.