# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
###### Abstract  
Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (\(\alpha\)), beta (\(\beta\)), gamma (\(\gamma\)), delta (\(\delta\)), and theta (\(\theta\)) as well as the low \(\beta\), high \(\beta\), and \(\theta\) to \(\beta\) ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future. Electroencephalography; machine learning; stroke prediction; real-time health monitoring; stroke disease analysis +
Footnote †: journal: Applied Sciences  
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Y.-A.-H., C.M.B., Pyo.-S., [PERSON], [PERSON], [PERSON], [PERSON] Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals  
Y.-A., Y.  
intraventricular hemorrhage. Cerebral hemorrhage occurs spontaneously without any external shock, and high blood pressure is reported as its main cause [4]. Stroke disease is one of the most common severe disorders that causes functional disabilities in adults and older people, which can lead to substantial difficulties with social or economic activities [5; 6; 7; 8]. For stroke patients, it is important to assess the current level of disability and to implement proper rehabilitation visits to medical institutions [9]. However, it is hard to classify stroke symptoms, making it difficult to properly diagnose disorders caused by stroke and any accompanying neurological damage. Therefore, there is a desperate need for technology that can keep track of potential stroke victims and support them during visits to medical institutions and allow them to receive diagnoses and treatments from medical staff as soon as possible. Current research is attempting to identify major risk factors for stroke by evaluating the initial disability of stroke patients and monitoring their conditions [10; 11]. Various methods for preventing the recurrence of these early disorders in stroke patients have been developed and studied, the Canadian Neurologic Scale and the National Institutes of Health Stroke Scale (NIHSS) are some of the scales used to diagnose the severity of stroke. In particular, the NIHSS developed by [PERSON] et al. is used as a measurement that is relatively easy and simple to perform at the beginning of hospitalization [12]. It is a proven tool used widely throughout the world.