# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
###### Abstract
Based on various studies for reliability and feasibility, 14 key categories have been chosen for evaluation by the medical staff for the NIHSS: consciousness level, facial paralysis, vision, upper and lower extremism, distal motion, limb motion, hearing impairment, and sensory touch. Although it only takes about six minutes to evaluate a patient according to the NIHSS, it has the limitation of not providing accurate predictive information results for the early detection of stroke or initial disability [12]. A study by [PERSON] et al. established a predictive model of stroke occurrence for Koreans [13]. Based on health examination data from the Korea National Health Insurance Corporation, [PERSON] et al. developed 10 year prediction models for the average risk of stroke using age, diabetes, smoking, total cholesterol, drinking volume, systolic blood pressure, exercise, and body mass index (BMI). However, that study follows the same method as the construction of the stroke risk prediction model by the Framingham Heart Study [14]. It is important to know what type of stroke (brain infarction, cerebral hemorrhage) has occurred and how much damage the brain has taken within three hours of the stroke outbreak [15]. Based on these risk factors, studies have attempted to predict stroke diseases using various statistical methods and machine-learning methods; these include research using logistic models such as Kannel [16] and studies based on [PERSON]'s proportional risk model [14] and the Weibull model [17]. However, it is difficult to apply these risk-based models to predict the occurrence of stroke diseases in Koreans. Therefore, there is a need to find a new model for stroke prediction that is appropriate for the elderly in Korea. In addition, clinical studies have reported that stroke recurrence rates vary depending on the type of stroke and risk factors, but the typical recurrence rate within a year is 10-15% [18]. Therefore, it is important to quickly predict the early onset in stroke patients and those with a stroke history. In this paper, we propose a health monitoring system that enables the real-time early detection of stroke and other diseases in older people based on EEG vital signals collected during daily activities. To accurately predict and to analyze the stroke disease, studies based on information on the features and patterns of EEG are being actively conducted [19; 20]. In this paper, important features in EEG vital signals were newly defined and used in experiments. Specifically, raw EEG biometric signals data from elderly people aged 65 or older were collected during walking and stored in real time. These real-time raw EEG data were preprocessed with decomposing functions or frequency components via Fast Fourier Transform (FFT). The extracted raw spectrum includes alpha (\(\alpha\)), beta (\(\beta\)), gamma (\(\gamma\)), delta (\(\delta\)), theta (\(\theta\)), low \(\beta\), high \(\beta\), and \(\theta\) to \(\beta\) ratio, each taken from six measurement locations (Fz, Oz, T7, T8, C1, and C2), meaning 66 important attributes were used in total. Our experiments have shown that the chosen attributes collected in real time while the elderly were walking can help detect and determine the precursor symptoms of more than 90% of stroke diseases in advance. Further, the Random Forest algorithm-based prediction model for stroke conditions with quartiles and a Z-score was experimentally identified with performance indicators such as accuracy and recall, preservation, and an F1-score of up to 92.51%. Thus, we present in-depth analysis results obtained through the performance and semantic interpretation of the elderly disease prediction and health monitoring system proposed in this paper, which are significant. Our initial results confirm that the proposed health monitoring system is able to detect and predict stroke precursors for older people in real time, and that it can be implemented at a low cost. The remaining parts of this paper are organized as follows.