# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
## 2 Related Works  
Electroencephalography (EEG) is a human physiological signal that measures human brain waves [19; 20; 21]. Most of the studies utilizing EEG data have used them to identify and analyze epilepsy diseases, but reports of unique brain waves have recently been found in other diseases, including schizophrenia, depression, and stroke [11; 21; 22]. Beyond the raw EEG spectrum, prior research has shown that the absolute power value, which is the value of the attribute depending on the frequency domain, and the relative power value, which represents the ratio of the total amplitude at the frequency band, are better indicators for determining brain dysfunction [22]. Relative power is based on the ratio of the total amplitude of the frequency band, independent of the electrical resistance to the skull thickness and other non-brain wave electrical activity. Thus, each signal can vary depending on the reader's noise screening ability, ability to compare absolute power with relative power, understanding of psychoanalysis, and neurological knowledge [23; 24]. Many studies have attempted to classify and predict various brain diseases using EEG data [25; 26; 27; 28; 29; 30; 31; 32]; these have included studies on brain function location [25; 26] as well as analyses of brain wave changes with human emotion [27; 28] and brain wave changes with sleep level [29; 30; 31]. There have also been a number of studies predicting brain diseases using various frequency attribute values, such as absolute power values or relative power values [22]. For example, there have been studies attempting to automatically classify or predict patients with epilepsy [31], detect and diagnose depression [32], and detect early Alzheimer's conditions [33]. Further, the environments required of vital-signal collection methods, including EEG, limit such methods to studies based on data collected through medical equipment, such as large hospitals, rather than data collected from the daily activities of the elderly. Several studies have reported the appearance of certain attribute values in stroke patients with EEG-based stroke analysis [34; 35; 36; 37]. For example, [PERSON] et al. [34] confirmed that the main properties of EEG with respect to stroke include the generation of abnormal and slow signals generated at the delta wave (\(\delta\)) wave frequency range (1-4 Hz) with the simultaneous reductions of normal and fast activities at the alpha (\(\alpha\)) wave frequency range (8-12 Hz). Through these experiments, we can confirm that relative delta power, delta and alpha power ratio, and the addition of delta and theta wave against alpha and beta wave ratios can be used to detect and predict stroke. [PERSON] et al. [35] studied EEG frequency analysis and topographic maps and found an increase in large delta waves and a decrease in alpha wave activity in 17 out of 20 mild stroke patients. [PERSON] et al. [36] confirmed that a rhythmic and high amplitude theta versus delta wave appeared in a patient with epilepsy during a stroke. [PERSON] et al. [37] confirmed that brain waves in stroke patients that were measured in the cerebral cortex affected the activity and stability of the theta wave and the delta wave, while the delta wave (0.1-3 Hz), alpha (7-13 Hz), beta wave (13-30 Hz), and high gamma wave (62-200 Hz) increased rapidly in the right hemisphere. Based on these studies, we believe that EEG research can help minimize medical costs and enable the early detection stroke diseases in the elderly during their daily activities. A quick literature review found a few studies using various machine-learning techniques, including artificial neural networks (ANN), for stroke diagnosis or prediction [38; 39; 40; 41; 42]. For example, [PERSON] et al. [38] reported that an individual's risk rate for stroke can be detected using ANN based on stroke patient data. Specifically, they used the backpropagation algorithm for learning, and showed improvements in consistency and diagnostic accuracy for the prediction. [PERSON] et al.