# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 4 Results
### ml and ml Article Distributions
After two-fold cross-validation, the model evaluation was reported as 100% sensitivity, 22% false detection, and 1.69 s delay latency. [PERSON] et al. [109] presented the recurrent neural network training with a reservoir-computing algorithm. The model was trained and tested with EEG signals from kainate-induced rats and genetic absence epilepsy rats (GAERS). After a ten-fold cross-validation, the GAERS presented a sensitivity of 96.4% and specificity of 96.2%. The kainate-induced rats had 93.9% sensitivity and 99.1% specificity. In an attempt to implement a RISC-like processor to detect and suppress epileptic seizure, [PERSON] et al. [108] trained a linear least square algorithm with the first ten hours of EEG signal from two rats and tested on the remaining forty-eight hours of the same rats. Accuracies of 92.33% and 92.81% were achieved for rat1 and rat2, respectively. Rat1 and rat2 had a delay latency of 0.63 s and 0.62 s, respectively. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Author** & **EEG Domain** & **Feature Extraction** & **Feature Type** & **Feature Selection** & **Feature Transformation** \\ \hline \([110]\) & FD & Wavelet & 12 linear & - & - \\ \([102]\) & TD, FD & - & 141 linear & - & PCA \\ \([112]\) & TD, FD & Gabor transform & 9 linear & Neighborhood component & - \\ \([109]\) & FD & Wavelet & 12 linear & Forward feature selection & - \\ \([103]\) & FD & Wavelet & 2 linear & Forward feature selection & - \\ \([108]\) & TD, FD & FFT & 2 linear, 1 nonlinear & - & - \\ \([107]\) & FD & FFT & 1 linear, 1 nonlinear & - & PCA \\ \([120]\) & TD & PCA, Laplacian & linear & - & PCA and Laplacian \\ \([34]\) & TD, FD & Eigenmap manifold & 19 linear & - & Eigenmap manifold \\  & & Natural visibility & & & \\ \([115]\) & TD & algorithm, & 14 linear & Sequential forward selection & - \\  & & Markov-binary & visibility graph, & & \\ \([106]\) & FD & FFT & 2 linear & - & - \\ \([116]\) & FD, TFD & FFT, wavelet transform & 1 linear, 1 nonlinear & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Previous work on feature types and feature engineering techniques. Table 3 summarises the recent studies on ML algorithms proposed for the detection of seizures in rodent models of epilepsy. Most articles used SVM compared to other algorithms applied in a single article. Impressive performance was achieved in recent articles from 2019 with the application of complex algorithms, namely, XGBoost, MLP, and GNN [106, 34, 115]. Machine learning-based seizure detection typically comprises feature extraction and classification stages. The tedious identification of appropriate features and the choice of classifier play pivotal roles in the performance of the ML seizure detection model [113]. The automatic extraction of features by the DL algorithm removes the hassle of feature extraction and poses a substantial advantage over classical machine learning [123]. Recently, DL has been investigated for seizure detection in rodent models of epilepsy. Table 4 summarises the DL techniques proposed for the detection of seizures in rodent models of epilepsy. To date, only one study has focused on seizure prediction using frontal lobe epileptiform discharges. [PERSON] et al. [116] examined the effectiveness of low-frequency wave ranges (0.5-13 Hz, commonly observed during sleep) and total frequency range (0.5-40 Hz) across four classifiers to predict seizures in WAG/Rij rats induced through kindling. The study did not state the number of rats in the study. However, five datasets were employed in the study. The classifiers examined included radial basis function-support vector machine (RBF-SVM), quadratic discriminant analysis (QDA), linear discriminant analysis (LDA), and linear support vector machine (LSVM). These classifiers were trained with wavelet coefficients and power spectral density features. The RBF-SVM classifier achieved the highest classification performance compared to all other classifiers across the five datasets.