# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
## 5 Conclusions  
We propose a new health monitoring system that detects and predicts the precursor symptoms of stroke diseases with the attribute information of power and relative values from raw data in the form of brain waves collected during elderly walking. Further, the prediction and analysis model in machine-learning attempts to analyze the results of real-time predictions and experiments of stroke disease in older people. In addition, to generalize machine-learning-based predictive models, the measurement positions are diversified to extract raw spectra, raw spectrum, alpha (\(\alpha\)), beta (\(\beta\)), gamma (\(\gamma\)), delta (\(\delta\)), and theta (\(\theta\)) values from six channels (Fz, Oz, T7, T8, C1, and C2) as well as low \(\beta\) and high \(\beta\). By extracting additional ratio values between \(\theta\) and \(\beta\), 66 new attributes were ultimately discovered and tested. Above all, the health monitoring system in this study can detect and predict the precursors of stroke, a fatal disease for elderly people, in real time, thus providing accurate prediction results in a system that can be implemented at a low cost. This is an important experimental result that can detect the possibility of an outbreak of stroke disease early and provide scientific rules that can interpret it. As a result, the system in this study has great advantages, as it can provide in-depth analysis information useful for older patients, particularly about everyday activities such as walking. Thus, the proposed system and experimental results in this paper imply that they are meaningful findings that can reduce the aftereffects of stroke as well as social and economic losses.  
In the future, research on stroke and other important diseases in the elderly should be conducted based on various real-time biological signals such as ECG and EMG as well as EEG. We will also study the prediction of precursors and outbreaks by disease and conduct in-depth analyses. We believe this will be a significant step toward developing more reliable and clinically useful health and disease prediction methods such as cancer and heart disease for the elderly through multimodal studies combining various vital signals, individual-specific electronic medical recording (EMR) data, and image information such as CT and MRI information.  
Figure 8: The C4.5 decision tree learning eight features of relative value with 10-fold CV (13 rules).  
**Author Contributions:** Conceptualization, Y.-AC. and J.Y.; methodology, Y.-AC., S.P., J.-A.J., C.M.B.H., C.-S.P., H.L. and J.Y.; software, Y.-AC. and J.Y.; validation, Y.-AC., S.P., J.-A.J., C.M.B.H., C.-S.P., H.L. and J.Y.; formal analysis, Y.-AC. and J.Y.; investigation, Y.-AC., S.P., J.-A.J., C.M.B.H., C.-S.P., H.L. and J.Y.; resources, Y.-AC. and J.Y.; data curation, Y.-AC. and J.Y.; writing--original draft preparation, Y.-AC., S.P., J.-A.J., C.M.B.H., C.-S.P., H.L. and J.Y.; writing--review and editing, Y.-AC., H.L. and J.Y.; visualization, Y.-AC. and J.Y.; supervision, J.Y. and C.-S.P.; project administration, C.-S.P.; funding acquisition, C.-S.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-05-ETRI).  
**Institutional Review Board Statement:** This paper was researched after review and approval by KRISS-IRB (Institutional Bioethics Committee of Korea Research Institute of Standards and Science).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.