# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 4 Results
### ml and ml Article Distributions
For the low-frequency signal, the maximum performance includes 94.62% accuracy, 98.46% sensitivity, and 90.76% specificity. The performance of the total frequency signals was 96.15% accuracy, 100% sensitivity, and 93.85% specificity. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Author** & **Number of Rodents** & **Classifier** & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Precision**} & \multicolumn{1}{c}{**Sensitivity**} & \multicolumn{1}{c}{**Specificity**} & \multicolumn{1}{c}{**AUC (\%)**} & \multicolumn{1}{c}{**False Alarm**} & \multicolumn{1}{c}{**Delay**} \\  & & & **Accuracy (\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
[104] & 7 & Deep residual neural network & - & - & 83.00 & 83.00 & 90.00 & - & - \\  & & Sequential dual deep & - & 98.00 & 100.00 & - & - & 6.00 & - \\
[114] & - & Fully connected neural network & 99.60–99.90 & - & 96.20–96.70 & 99.60–99.90 & 98.90– & 0.9–1.1 & - \\
[113] & 32 & neural network & 99.60–99.90 & - & 96.20–96.70 & 99.60–99.90 & 99.30 & 0.9–1.1 & - \\
[105] & 11 & CNN & 94.28 & - & 96.60 & 90.40 & - & - & - \\
[117] & 8 & GoogleNet & 81.10–95.60 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recent DL algorithms for detecting seizures in rodent models of epilepsy. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Author** & **Number of Rodents** & **Classifier** & **Accuracy** & \multicolumn{3}{c}{**Precision**} & \multicolumn{1}{c}{**Specificity**} & \multicolumn{1}{c}{**AUC (\%)**} & \multicolumn{1}{c}{**False Alarm**} & \multicolumn{1}{c}{**Delay**} \\  & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Latency (s)** \\ \hline
[110] & 11 & SVM & - & 59.00 & 68.00 & - & - & - & - \\
[102] & 11 & GLM & - & 59.00 & 68.00 & - & - & 99.50 & - & - \\
[112] & 13 & RF & 92.80–97.20 & 77.30–97.40 & 93.03–95.80 & 91.90–98.90 & - & - & - \\
[103] & 45 & RC-BRR & - & - & 96.20 & 98.20 & - & 9.10 & 0.97 \\
[107] & 3 & RF-SVM & 97.50 & - & 97.03 & 97.83 & - & - & - \\
[120] & - & kRR & 73.43–97.76 & - & - & - & - & - & - \\
[34] & 26 & XGBoost & 93.1–98.8 & - & 76.30–98.8 & 93.10–98.80 & - & - & - \\
[115] & 27 & MLP & 92.13 & - & 98.94 & - & 86.03 & - & - \\
[106] & 18 & GNN & - & - & 98.01 & - & 98.90 & 95.90 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recent ML algorithms for detecting seizures in rodent models of epilepsy. RQ4: What Training Methodologies and Evaluation Metrics Have Been Used in the Rodent Models of Epilepsy, and Which of the Developed DL/ML Models Have Been Implemented? Previous studies have used various types of rodents to analyse epilepsy. Genetic rodent models are widely employed for studying absence seizures, with temporal lobe epilepsy and Dravet syndrome being extensively investigated (see Table 1). The number of rodents used in these studies ranges from 3 to 45 for model development. The number of mice used in developing ML and DL algorithms in recent studies is shown in Tables 3 and 4. Various training strategies have been observed in the literature. Most studies relied on the train-test split [102; 103; 106; 108; 110; 111; 118]. [PERSON] et al. [100] and [PERSON] et al. [109] used ten-fold cross-validation, but [PERSON] et al. [120] added an independent test set to the disjointed ten-fold cross-validation. [PERSON] et al. [119] and [PERSON] et al. [101] employed two-fold cross-validation. Leave-one-out cross-validation was used by [PERSON] et al. [107] and [PERSON] et al. [105]. [PERSON] et al. [104] utilised leave-one-out cross-validation with an independent test set. Singularly, [PERSON] et al. [112] split the dataset in 100-fold cross-validation with independent test data. Some recent studies adopted train-validate and evaluate with independent test sets [34; 113; 114; 115]. Two studies did not specify the training and validation type employed in their study [116; 117]. Figure 6 shows the number of eligible articles employing different performance valuation metrics.