# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
## 3 Elderly Stroke Monitoring System Based on Machine Learning and EEG
### Stroke Prediction Module for the Elderly Based on Machine Learning  
The module proposed in this paper uses real-time EEG biometric signals to detect and predict elderly stroke precursors in advance, and it consists of two submodules in total. The offline submodules provide predictive models via Machine-Learning (ML) Training, which performs machine-learning model-specific learning from the preprocessing of brainwave signals. Meanwhile, the online sub-module provides medical staff with the risk of stroke in older adults based on real-time EEG data as shown in Figure 4.  
First, the offline module consists of a total of four sub-blocks: (1) the biometric repository stores various biological signals generated while the elderly participate in daily activities. For example, this block collects, stores, and manages EEGs, electrocardiograms (ECGs), pulse waves, and EMGs. (2) In the preprocessing of brain waves, the null and missing values of raw values collected in real time from the six channels are corrected or deleted. (3) Fast Fourier Transform (FFT) performs the task of decomposing functions or signals into frequency components, specifically by transforming the raw value of brain waves into individual spectral components. Next, the frequency information for the signal is extracted in real time. For example, alpha, beta, etc. are extracted, as shown in Table 2. Further, a learning model can be developed using both absolute power and relative values for \(11\) properties extracted per six channels. Finally, as presented in Tables 2 and 3, learning and prediction models are developed by selecting the optimal set of attributes used in real-time stroke disease prediction. (4) The machine-learning-based learning module implements learning based on attributes extracted from EEG biometric signals collected in real time. At this time, the prediction model learned from machine-learning algorithms in this block uses brainwave data from older people collected in real time during everyday activities. In \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Features** & **Meaning and Explanation** \\ \hline
1 & Fz(Theta) & Theta route value in the data \\
3 & Fz(IDAR) & Inverse ratio of DAR of Fz channel \\
4 & Fz(IRRI) & Power ratio index of Fz channel \\
5 & T7(RRI) & Power ratio index of T7 channel \\
6 & C1(DAR) & Ratio of mean power of C1 channel \\
7 & Oz(IDAR) & Inverse ratio of DAR of Oz channel \\
8 & T8(RRI) & Power ratio index of T8 channel \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal feature subset extracted from the power value of EEG. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Features** & **Meaning and Explanation** \\ \hline
1 & Fz(Alpha) & Alpha power of Fz channel (8–13 Hz) \\
2 & Fz(Beta) & Beta power of Fz channel (14–30 Hz) \\
3 & Fz(Gamma) & Gamma power of Fz channel (30 Hz or more) \\
4 & Fz(IDAR) & Inverse ratio of DAR of Fz channel \\
5 & T7(Theta) & Theta power of T7 channel (4–8 Hz) \\
6 & T7(Alpha) & Alpha power of T7 channel (8–13 Hz) \\
7 & T7(Gamma) & Gamma power of T7 channel (30 Hz or more) \\
8 & C1(Theta) & Theta power of C1 channel (4–8 Hz) \\
9 & Oz(Beta) & Beta power of Oz channel (14–30 Hz) \\
10 & Oz(Gamma) & Gamma power of Oz channel (30 Hz or more) \\
11 & C2(Beta) & Beta power of C2 channel (14–30 Hz) \\
12 & C2(Gamma) & Gamma power of C2 channel (30 Hz or more) \\
13 & C2(Low\_Beta) & Low beta power of C2 channel (12–25 Hz) \\
14 & T8(Theta) & Theta power of T8 channel (4–8 Hz) \\
15 & T8(Gamma) & Gamma power of T8 channel (30 Hz or more) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal feature subset extracted from the relative value of EEG. addition, the learned predictive model is passed to the online module so it can be used to determine the degree of precursors and predictions for stroke. The online module also consists of a total of four sub-blocks: (1) Real-time data on brain waves are collected in the form of biological signals during everyday life, such as walking, from the elderly.