# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
## 3 Elderly Stroke Monitoring System Based on Machine Learning and EEG
### Attribute Definition and Extraction in EEG  
The experiments in this paper require raw data to be collected in the form of EEG signals during walking scenarios, as it does not use segment data from various experimental protocols. We conduct experimental and stroke disease prediction model studies by extracting the important properties from the raw values of these EEG signals. FFT was applied from the raw value of the EEG signal from each channel to extract the measurement variables raw spectrum alpha (\(\alpha\)), beta (\(\beta\)), gamma (\(\gamma\)), delta (\(\delta\)), and theta (\(\theta\)), as well as the properties between the value's low \(\beta\), high \(\beta\), and theta-to-beta ratio. In this experiment, 66 attributes in total were newly defined and extracted in the form of the absolute power and relative values of waveforms (signals) from the raw EEG spectrum. The absolute power value and relative value used in the experiment in this paper consist of 11 attribute values for each of the six channels (Fz, Oz, T7, T8, C1, and C2), as listed in Table 1, which ultimately account for 66 attribute values and class.  
Choosing an efficient subset of attributes for pattern classification is one of the most important research steps [46; 47]. In this paper, we use the method described by [PERSON] [48], which has already proven its performance in various fields of attribute subset selection for power and relative values of EEG. A merit function (Equation (1)) is used to evaluate how efficiently each subset of \(F_{s}\subset F\) expresses the entire attributes. The subset with the largest value of the merit function is determined by the subset that best represents the entire property [48].  
\[Merit(F_{s})=\frac{k\overline{r_{cf}}}{\sqrt{k+k(k-1)\overline{r_{ff}}}} \tag{1}\]  
where k is the number of attributes in subset \(F_{s}\), \(\overline{r_{cf}}\) is the mean distribution of attributes contained in \(F_{s}\), and \(\overline{r_{ff}}\) is the mean correlation value of the attributes. Tables 2 and 3 present the optimal subset of attributes chosen from absolute power value and relative  
\begin{table}
\begin{tabular}{c c c c} \hline \hline No.Contents & Features & \multicolumn{1}{c}{**Meaning and Explanation**} \\ \hline  & & Delta (\(\delta\)) & Delta power (1–4 Hz) \\  & & Theta (\(\theta\)) & Theta power (4–8 Hz) \\  & & Alpha (\(\alpha\)) & Alpha power (8–13 Hz) \\  & & Beta (\(\beta\)) & Beta power (1–30 Hz) \\
1 & 6 Channel & Gamma (\(\gamma\)) & Gamma power (30 Hz or more) \\ \(\sim\) & (Fz, Oz, T7, T8, & Low\_Beta & Low beta power (12–25 Hz) \\
66 & C1, C2) & High\_Beta & High beta power (25–30 Hz) \\  & & Theta\_to\_Beta & Value of the beta ratio in theta (extracting abnormal theta waves) \\  & & Delta divided by & Ratio of mean power (Delta/Alpha) \\  & & Alpha (DAR) & Inverse ratio of DAR (Alpha/Delta) \\  & & IDAR & PRI power ratio index (delta+theta \\  & & PRI & to alpha+beta), \\  & & Low frequency to high frequency \\
67 & Class Labeling & Normal or Stroke Elderly \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed Descriptions of Newly Defined and Extracted EEG Attributes.  
value using [PERSON]'s method. Table 2 lists a subset of eight attributes selected from the power value, and Table 3 lists a subset of 15 optimal attributes selected from the relative value.