## 2 Literature Review
### Related Works  
In [38], [PERSON] utilized the bivariate pattern classification method for developing an online retraining technique that can gradually scale the training set. Their proposed model reduced false alarms using a post-processing scheme. Three datasets were used to evaluate the model, including the ECoG Freiburg database, the CHB-MIT EEG database, and the national Taiwan university hospital database. The results showed an improvement in sensitivity by 29.0 in the ECoG database and 17.4 in the CHB-MIT EEG database compared with the offline training method. [PERSON] [39] developed an algorithm for recognizing epileptic seizures automatically. They utilized discrete wavelet transform (DWT) for feature extraction and decomposing EEG signals. They compared the performance of normalized coefficient of variation (NCOV), which was computed on the coefficients extracted from signals range of 0-32 Hz, with a typical coefficient of variation (COV), and the results show the advantage of NCOV \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Model Type** & **Batch Algorithm** & **Stream Algorithm** \\ \hline Decision Tree & Single & C4.5 [28] & VFDT [25; 29] \\ Nearest Neighbors & Single & K-NN & Adaptive-NN [30] \\ Boosting & Ensemble & AdaBoost [31] & OzaBoost [32] \\ Bagging & Ensemble & Random Forest [33] & ARF [34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Equivalent of four popular classification techniques for classifying the data stream. according to the evaluation on five subjects from CHB-MIT EEG dataset using Linear discriminant analysis (LDA). In [40], [PERSON] utilized a linear classifier with EEG-extracted features using wavelets and some other statistical features (mean, minimum, maximum, entropy, and standard deviation). EEG datasets from Bonn University and CHB-MIT are used in their implementation. The accuracy of seizure onset detection was 84.2 on the CHB-MIT dataset. In [41], [PERSON] reduced the hardware complexity required for predicting epileptic seizures from EEG signals. Overall, 44 extracted features were ranked and selected, then processed using a second-order Kalman filter. A Support Vector Machine (SVM) classifier was used in their model, and the implementation included two datasets: the Freiburg dataset and the CHB-MIT dataset. The results showed a sensitivity of 100 for the Freiburg dataset and of 98.68 for the CHB-MIT dataset. A prediction method for patient-specific seizures was presented by [42], which applied Convolutional Neural Networks (CNNs) to the EEG dataset. The features were extracted by using Short-Time Fourier Transform (STFT); then, a standardization process was used to prevent any possible influence between high and low frequencies. CNN was used in their model in both features selection and classification steps. The best result of their proposed model was an 82.3 sensitivity. [PERSON] [43] compared the performances of frequency and time domain for the detection of epilepsy in two datasets: the Freiburg dataset and the CHB-MIT EEG dataset. The comparison included two binary classification tasks and one three-class task. The best result of the frequency domain experiments was an accuracy of 96.7% with the Freiburg dataset and of 97.5% with the CHB-MIT EEG dataset. It was 91.1% accurate in the Freiburg dataset and 62.3% accurate in the CHB-MIT EEG dataset with time-domain signals experiments. A cloud-based healthcare system was proposed by [44]; it utilized Parallel Particle Swarm Optimization (PPSO) to improve the selection of Virtual Machines (VMs). The implementation of their model included using linear regression and a neural network model for chronic kidney disease (CKD) diagnosis and prediction. Their results showed an improvement in the time of data retrieval in addition to a reduction in the total execution time. Common Spatial Patterns (CSP) and Convolutional Neural Networks (CNN) are utilized by [45] for seizure prediction.