# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
###### Abstract
In particular, there are many issues with estimating the predictability of a precursor or the predictability of a stroke disease based only on the risk factors. The Framingham Heart Study, which was based on a forward-looking cohort study of cardiovascular disease, proposed a stroke risk prediction model [12; 13]. However, a mismatch was observed with this model between the rates of heart disease predicted using a European population and those predicted using a Chinese population [13; 14; 15; 16]. Therefore, there may be many differences in the socio-cultural environments and genetic characteristics of the subjects of the Framingham Heart Study. As a result, this limits the potential application of this model of previous research to Koreans and the elderly. In addition, stroke and the accompanying neurological damage represent a difficult domain to detect and predict early due to its various symptoms and classifications. [PERSON] et al. [17; 18] developed a stroke prediction model specifically for Koreans. This model was informed by 10 years of health examination data provided by the National Health Insurance Service (NHIS) [17]. However, like the stroke prediction model built by the Framingham Heart Study, it still has several disadvantages. These approach is based on past health examination data, i.e., risk factor. Since these provide the predicted probability of occurring a disease in the long distant future of about 5 or 10 years, it is difficult to apply to the various symptoms and predictions of the elderly (patient) before the onset of stroke at the present time. This paper proposes a new stroke detection and prediction system based on real-time electromyography (EMG) data obtained from everyday activities. This system collected real-time EMG data during physical activities and used it for real-time prediction of stroke with the aid of Random Forest and Long Short-Term Memory (LSTM) algorithms. For improved prediction accuracy and validation of the system, the left and right biceps femoris and gastrocnemius muscle were measured and collected in real-time from a healthcare device at 1500 Hz. This system overcomes the limitations of previous studies in that it provides probability values for stroke disease in the next 10 years. In addition, it was experimentally verified that accurate detection and prediction of stroke disease is only possible with EMG bio-signals collected in real time. In this paper, our results showed stroke prediction accuracy values of 90.38% for the Random Forest algorithm and of 98.958% for LSTM for participants while walking. This proof of concept indicates that a stroke can be detected using real-time bio-signals. The continuous monitoring of EMG ensures that the model is constantly updated, thus reducing misdiagnosis and allowing for quick responses from medical staff or hospitals. The prediction model of this proposed system can be used as a basic model for the pre-detection and the rapid prediction of other diseases such as heart disease. This paper is organized as follows. Section 2 provides a background on the original research methods for stroke diseases. Section 3 introduces the artificial intelligence-based real-time stroke disease prediction model examined in this paper. Section 4 analyzes the experimental results and performance of the model, and finally, Section 5 concludes this paper and discusses future research tasks.