## 4 Proposed Epileptic Seizures Detection Model
### Data Collection--EEG Dataset  
This research utilized the UCI Epileptic Seizure Recognition Dataset [1], a publicly available dataset widely acknowledged for its relevance and diversity in EEG recordings. This dataset served as the cornerstone for both the development and evaluation phases of our seizure detection models. Its comprehensive collection of EEG signals enabled the robust experimentation and validation of various ML algorithms tailored for seizure detection. The UCI Epileptic Seizure Recognition Dataset is structured to facilitate systematic analysis and interpretation of EEG data. It comprises five distinct folders, each representing a unique category delineating different physiological states. Within each category, there are precisely 100 files, each containing the EEG recordings of an individual. Figure 2: Proposed Epileptic Seizures Detection Model. The EEG recordings within these files are standardized to a duration of 23.6 s, providing a consistent temporal frame for analysis. These recordings are sampled into 4097 data points, ensuring granularity in capturing EEG patterns. To facilitate analysis at a finer temporal resolution, these 4097 data points are further segmented into 23 chunks, each spanning 1 s and containing 178 data points. Overall, the dataset encompasses EEG recordings from 500 individuals, resulting in a total of 11,500 rows or instances of recorded data. These instances capture the EEG profile at various moments in time, offering insights into the dynamic nature of epileptic seizures and serving as the foundation for our study's analysis and model development. This dataset's structured organization and rich information content make it an invaluable resource for researchers seeking to investigate epileptic seizure detection using EEG signals. Its utilization in our study enabled rigorous experimentation and validation, contributing to the advancement of seizure detection methodologies. The structure and composition of the UCI Epileptic Seizure Recognition Dataset comprises 5 distinct directories, each containing 100 files. Each file represents the recorded brain activity of a single subject, captured over a precisely timed duration of 23.6 s. The EEG recordings within each file consist of 4097 data points, capturing brain activity at discrete moments. These data points collectively represent observations from 500 individuals, where each person's recording spans 4097 data points corresponding to 23.5 s of EEG data. To effectively manage the sequential EEG data, the dataset is partitioned into 23 segments, each containing 178 data points, representing precisely 1-s intervals. Each data point within these segments signifies an EEG recording captured at distinct time instances. As a result, the structured dataset comprises 11,500 rows, with each row consisting of 178 data points representing 1-s EEG intervals. The final column, positioned as the 179 th column, serves as the 'y' label, indicating values 1, 2, 3, 4, 5. Here, 'y' represents the response variable, while the explanatory variables range from X1 to X178, facilitating the training and preparation of the dataset for subsequent modelling and analysis stages. In statistical modelling and ML contexts, the response variable 'y' is also known as the dependent variable, target variable, or outcome variable. The explanatory variables (X1 to X178) are commonly referred to as independent variables, predictor variables, features, or attributes in the dataset. The random illustration depicting a sample view of the Epileptic Seizure Recognition Dataset is represented in Figure 3.  
The structured organization of the dataset ensures that each instance captures the EEG profile at various moments in time, offering insights into the dynamic nature of epileptic seizures. The use of this dataset enabled rigorous experimentation and validation, contributing to the advancement of seizure detection methodologies.