# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
## Related Works
### Stroke Prediction Using Traditional Techniques  
Successful research has been conducted to monitor the conditions of stroke patients and discover major risk factors for stroke. This knowledge can be used to prevent the recurrence of stroke and assess patient stroke severity [22; 23]. As methods of assessing stroke patients' severity, the European Stroke Scale, the Canadian Neurologic Scale, and the National Institutes of Health Stroke Scale (NIHSS) have been published since the Mathew scale was first published in 1972 [22; 23]. Of these, the NIHSS is widely used around the world and has been proven in terms of reliability and validity. The NIHSS scale consists of 14 items: level of consciousness; examination; vision; facial paralysis; upper and lower extremities; impaired limbs, senses, and speech; oral disorders; neglect; and distal movement. It takes about 6.6 min for a patient to be measured on the NIHSS. A modified NIHSS [24] that quantitatively evaluates post-stroke disorders and simplifies the measurement process, particularly in the early stages of hospitalization. While these NIHSSs can comprehensively assess the severity of disability resulting from stroke, they cannot be used for the early detection of stroke, as they do not provide accurate predictions. A stroke prediction model for Koreans was developed by [PERSON] et al. [25]; this model can determine the risk of stroke occurrence for 10 years based on health examination data from the National Health Insurance Service (NHIS), including age, diabetes, hypertension, smoking, total cholesterol, exercise, body mass index, and drinking volume. However, that study has limitations similar to those of the Framingham heart study model, as the same method was used to create the stroke risk prediction model [12; 13]. The main issue with these models is that they do not take into account some major risk factors of stroke and death causes other than stroke, i.e., competing risk. From the onset of a stroke, patients must receive professional treatment within three hours. In this three-hour period, the type of stroke (ischemic stroke or hemorrhagic stroke) and the severity of the stroke need to be determined to administer the right treatment [12; 13; 26]. Therefore, in order to accurately detect and predict these factors during daily life, it is necessary to not only apply PHR and EMR, but also to analyze information regarding patterns and bio-signals in daily life from a healthcare device. In other studies, major risk factors for stroke were identified through prior studies and clinical trials, and these risk factors were reported to include smoking, hypertension, systolic blood pressure, diabetes, and obesity [12; 13]. In other words, stroke is more likely to be caused by the interaction of various risk factors rather than any one factor. Thus, a new methodology is emerging that can assess the risk factors of each individual to predict stroke early. Based on these risk factors, research was conducted with various statistical methods such as a logistic model [27], [PERSON]'s proportional risk model [12; 13], and the Weibull model [28]. However, these risk-based advanced studies are not appropriate for predicting the risk of stroke in Koreans. There is a particular need to find a new model that can be used to predict stroke risk for Korean elderly people. The onset of a stroke can be sudden, and it is reported to be nine times more likely for a stroke to reoccur than it is to occur in those with no history of stroke [29]. In addition, clinical studies have reported that stroke recurrence rates vary depending on the type of stroke, the race of the person, and various risk factors, but within one year, stroke generally has a recurrence rate of 10-15%. It is therefore important to quickly detect and predict the initial outbreak of stroke as well as possible recurrence in people with prior stroke history [30]. Clinical studies have indicated that initial changes in physiological variables are potential therapeutic targets for stroke.