# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 4 Results  
### ml and ml Article Distributions  
Figure 4 shows the number of yearly published articles that employed ml and ml techniques. The usage of these techniques started with classical ml in 2006 and continued with a steady frequency until 2011. DL emerged in rodent EEG seizure detection and prediction in 2019. However, it is noticeable that the frequency of ml articles in rodent EEG analysis has not surged since its first application. Distribution of Articles among Epilepsy and Seizure/Epilepsy Types Have Been Automatically Analysed with ML or DL Algorithms? Different seizure types have been induced through various rodent models of epilepsy. Several previous works [34; 100; 101; 102; 103; 104] evaluated temporal lobe epilepsy, and eight studies focused on absence seizures in genetic absence epilepsy rats [105; 106; 107; 108; 109; 110; 111]. Two studies analysed Dravet syndrome [34; 112] and tonic-clonic seizures [109; 113], respectively. Convulsive seizures [114], clonic seizures [115], frontal lobe epilepsy [116], and post-traumatic epilepsy [117] were each analysed by one study, respectively. The majority of the studies analysed a seizure type, nine studies focused on seizures of generalised onset (absence and generalised tonic-clonic seizures), while seven studies focused on detecting seizures in focal onset seizure types. Among the studies reviewed, three investigated two seizure types [34; 109; 103], whereas others [118; 119; 120] did not specify the type of seizure analyzed. Refer to Table 1 for a comprehensive list of seizure types and the corresponding rodent models of epilepsy. Figure 4: The annual number of published articles using ML and DL techniques in rodent EEG analysis from 1 January 1994 to 1 January 2024. Figure 5: Distribution of eligible articles focusing on seizure detection and prediction in rodent models of epilepsy, with only 1 article on seizure prediction and 21 on seizure detection. RQ2: What Features and Feature Engineering Techniques Have Been Considered in the Classical Machine Learning Detection and Prediction of Seizures in Rodent Models of Epilepsy? Studies have investigated different features and feature extraction techniques. [PERSON] et al. [111] analysed four linear time-domain features, mean value, curve length, accumulated energy, and sixth power, and one nonlinear dynamics feature, average nonlinear energy. Contrary to extracting features, in an extension study, [PERSON] et al. [118] used the weighted locally linear embedding (WLLE) and locally linear embedding (LLE) in reducing the dimension of the EEG time-domain signal. [PERSON] et al. [100] explored the three domains of EEG signal and extracted ten features; one FD feature (periodic frequency) was extracted using the fast Fourier transform, while Daubechies wavelet decomposed the time-domain to acquire detailed and approximate coefficients from wavelet levels 1-4 and one TD feature (mean of the absolute amplitude). In addition to the mean energy and mean curve length, wavelet transform was used to extract the wavelet energy in the beta band (12-20 Hz) by [PERSON] et al. [119]. [PERSON] et al. [101] used digital filters to exclude and study the spectral power of ten frequency bands, together with approximate entropy, a nonlinear feature that measures the predictability of a time series. [PERSON] et al. [102] extracted 141 linear features from TD and FD. The high-dimensional data were reduced using principal component analysis (PCA). Studies based on deep learning investigate the signals directly in the time and frequency domains without the need to extract features from these domains [104; 105; 113; 114; 117]. Table 2 details the feature extraction techniques and the type of EEG features adopted in the ML detection of seizures in rodent models of epilepsy. Time-frequency domain features were used in one study [116]. Most articles extracted their features from either TD or FD or both. The linear features have been intensively used compared to the nonlinear features.