## 3 Materials and Methods
### Feature Selection
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Feature Name** & **Formula** & **Description** \\ \hline
6 & Root Mean Square (RMS) & \(x_{rms}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}{(x_{i})^{2}}}\) &
\begin{tabular}{c} This is used to measure the size of \\ changing quantities. \\ \end{tabular} \\ \hline
7 & Skewness & \(x_{skewness}=\frac{\sum\limits_{i=1}^{N}{(x_{i}-x_{m})^{3}}}{(N-1)x_{std}^{3}}\) &
\begin{tabular}{c} This is the symmetry of the distribution of \\ values in a population. \\ \end{tabular} \\ \hline
8 & Kurtosis & \(x_{kurtosis}=\frac{\sum\limits_{i=1}^{N}{(x_{i}-x_{m})^{4}}}{(N-1)x_{std}^{4}}\) &
\begin{tabular}{c} From all the values collected, the gradient \\ of the distribution pattern determines \\ the steepness. \\ \end{tabular} \\ \hline
9 & Variance (Var) & \(x_{var}=\frac{1}{N}\sum\limits_{i=1}^{N}{(x_{i}-x_{m})^{2}}\) &
\begin{tabular}{c} This shows the degree to which the column \\ numbers vary. \\ \end{tabular} \\ \hline
10 & Peak-to-Peak (P2P) & \(\max(|x_{i}|)-\min(x_{i})\) &
\begin{tabular}{c} The amplitude range of the signal is \\ quantified by computing the difference \\ between its maximum and minimum \\ values, providing a measure of the signal’s \\ overall variability. \\ \end{tabular} \\ \hline
11 & Impulse Factor (IF) & \(IF=\frac{\max(|x_{i}|)}{\sqrt{\frac{1}{N}\sum_{i=1}^{N}{(x_{i})^{2}}}}\) & Ratio of AbsMax to RMS. \\ \hline
12 & Crest Factor (CF) & \(CF=\frac{\max(|x_{i}|)}{\sqrt{\frac{1}{N}\sum_{i=1}^{N}{\left|x_{i}\right|}}}\) &
\begin{tabular}{c} The ratio of the average square root of a \\ wave to its peak is known as its RMS value. \\ \end{tabular} \\ \hline
13 & Shape Factor (SF) & \(SF=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}{(x_{i})^{2}}}}{\frac{1}{N}\sum_{i=1}^{N }{\left|x_{i}\right|}}\) &
\begin{tabular}{c} The ratio of the average value of a wave’s \\ amplitude to that wave’s average square \\ root value. \\ \end{tabular} \\ \hline
14 & \
\begin{tabular}{c} Mobility (Hjorth \\ Parameters) \\ \end{tabular} & \(Mobility=\sqrt{\frac{\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}{(x_{i})^{2}}}}{ \frac{1}{N}\sum_{i=1}^{N}{\left|x_{i}\right|}}}\) &
\begin{tabular}{c} The square root of the ratio of the signal \\ variances derived from the TD to the \\ signal itself. \\ \end{tabular} \\ \hline
15 & \
\begin{tabular}{c} Complexity (Hjorth \\ Parameters) \\ \end{tabular} & \(Complexity=\frac{Mobility\ (x^{\prime}(t))}{Mobility\ (x(t))}\) &
\begin{tabular}{c} This refers to the change in frequency by \\ comparing the similarity of the signal to a \\ pure sine wave. \\ \end{tabular} \\ \hline \end{tabular} Note: \(x\) is the signals series.  
\end{table} 
Table 3: _Cont._nology (ML optimization), and security (intrusion detection) [38; 39; 40]. Its significance lies in its ability to improve estimation accuracy and precision by eliminating irrelevant and redundant data, which in turn reduces model training time and overfitting. Feature selection simplifies models, reducing complexity and memory requirements while ensuring higher performance. By selecting a subset of relevant, noise-free data, the time required for model training decreases, and the model becomes less complex and easier to interpret. Before feature extraction, it is crucial to clean the dataset by removing low-variance variables, handling missing values, and addressing categorical variables with identical or excessive levels [9]. Filter methods for feature selection utilize statistical criteria to assess and rank features according to their relation to the target variable, independently of any learning algorithm [36]. Filter methods serve as a preprocessing step to rank features based on their relevance, which are then selected and revealed by an estimator using various variable sorting techniques [25]. As they are applied before the classification process, they are called filter methods. After ranking features, the top-performing ones are selected for use in model training or further analysis.