# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 5 Discussion
### Research Gaps
The seizure detection process is challenged by the intractable issue of imbalanced class distribution. This phenomenon arises due to the inherent bias of the classification algorithm towards favouring the majority class, which in this case is the nonseizure data [192]. To tackle the class imbalance issue, the studies included in this survey balanced the training data by reducing the number of nonseizure events through downsampling. Contrary to downsampling, oversampling involves making exact copies of the minority class, which can lead to overfitting [193; 194]. Moreover, it is important to emphasise that oversampling does not yield any supplementary data and, hence, does not successfully address the fundamental issue of inadequate data; this explains why just increasing the sample size of the minority class does not effectively improve the identification of that class [194; 195] and why downsampling the majority class seems more beneficial than oversampling [196]. However, downsampling can lead to a fabricated balance that does not precisely reflect the true distribution of categories in real-world scenarios [197]. This may lead to a model that demonstrates exceptional performance on a training set with evenly distributed data but performs poorly when presented with imbalanced real-world data. Lack of sufficient training data hinders detecting epileptiform activity in rodent models of epilepsy. Downsampling further decreases the data size for the majority class, decreasing the statistical power and dependability of the model's classifications [198]. Additionally, when the data size is reduced, the model's classification can become more responsive to changes in the training data, resulting in heightened variability in classification and a less reliable model [199]. Therefore, cost-sensitive learning may improve performance measures for the minority class. Cost-sensitive methods effectively handle class imbalance by imposing higher penalties for misclassifying the minority class. When identifying epileptiform activity, it is essential to prioritise signals from the ictal and preictal stages over those from the interictal and postictal states. Misclassifying ictal or preictal signals carries more severe consequences than misclassifying interictal signals. Although interictal events offer useful insights into brain activity during the intervals between seizures, the identification of ictal and preictal events is probably more essential due to their immediate and substantial impact. By prioritizing the identification of ictal or preictal events, model performance may improve amidst class imbalance, providing preclinical researchers with the capacity to investigate the underlying mechanisms of epilepsy, predict seizure onset, and evaluate the impact of drug trials on rodents. In this review, SVM, ANN, and DL models in rodent epileptiform discharge detection have shown outstanding performance, as seen in Table 4. The prevailing notion is that complex ML/DL models offer enhanced performance [200] as a result of their complex computational capability in discovering nonlinear relations in EEG signals and several application domains. These models are frequently considered black boxes [201], which are nontransparent and prove difficult for nontechnical people to understand their decision-making process. Government agencies have debated the lack of transparency regarding the design, development, evaluation, and implementation of AI tools. This might not be a challenge in some fields, but the performance of algorithms and the ease of understanding ML and DL models are very important for their use in the medical domain [202], which results in model transparency that enhances the trust of the users. However, there is often a compromise between how well a model works and how easy it is to understand [203]. High-performance models are often more complex and opaque, while interpretable models may not have good performance [204].