# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
###### Abstract  
Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. If left untreated, stroke can lead to death. In most cases, patients with stroke have been observed to have abnormal bio-signals (i.e., ECG). Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can receive appropriate treatment quickly. However, most diagnosis and prediction systems for stroke are image analysis tools such as CT or MRI, which are expensive and difficult to use for real-time diagnosis. In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). Both machine learning (Random Forest) and deep learning (Long Short-Term Memory) algorithms were used in our system. EMG (Electromyography) bio-signals were collected in real time from thighs and calves, after which the important features were extracted, and prediction models were developed based on everyday activities. Prediction accuracies of 90.38% for Random Forest and of 98.958% for LSTM were obtained for our proposed system. This system can be considered an alternative, low-cost, real-time diagnosis system that can obtain accurate stroke prediction and can potentially be used for other diseases such as heart disease. Keywords: electromyography (EMG); stroke prediction; stroke disease analysis; artificial intelligence; machine learning; random forest; deep learning; long short-term memory (LSTM) +
Footnote †: journal: Medicalintelligent medical solutions, such as precision healthcare and predictive healthcare services, that can be used to prevent diseases. However, it is still difficult to derive meaning information between various types of healthcare big data. The recent improvements in computing infrastructure and the emergence of various AI frameworks in ICT have made AI-based digital healthcare analysis more intelligent and more feasible for this smart healthcare era. The smart healthcare system is evolving into a service that combines medical big data with ICT to help individuals manage their health remotely. According to the World Health Organization (WHO), malignant neoplasms (cancer) and heart disease were the first- and second-leading causes of death in 2016, respectively. The third-leading cause of death in 2016 was stroke disease, accounting for 5.7 million deaths [5]. In 2016, stroke accounted for 11% of all deaths worldwide. A stroke occurs when there is an interruption of blood flow to the brain, resulting in necrosis of brain cells [6; 7]. In general, stroke is more likely to occur in the elderly, and stroke can lead to cerebral dysfunction such as hemipleggia, mispronunciation, and lack of consciousness. These can cause severe disability in adults and even death [6; 7]. Stroke is a treatable disease, and if detected or predicted early, its severity can be greatly reduced. Various studies and clinical trials have reported several risk factors of stroke [8; 9; 10]. Properly managing and treating adjustable risk factors such as hypertension, smoking, diabetes, and obesity can decrease stroke occurrences [10]. However, a survey of people with hypertension by the National Health and Nutrition Examination Survey (NHANES) found that about 30% of people were unaware that they had hypertension, 15% of patients did not take any medication, and 26% of hypertension cases were not yet well-controlled [11]. Therefore, medical and personal efforts are needed to improve this situation, and there is an urgent need to conduct research and implement national measures to prepare for the increase in other stroke diseases that is expected with the global aging trend. It can be difficult to predict stroke symptoms or outbreaks using risk factors, because the definitions used for individual risk factors or the methodologies for correlation between the likelihood of a particular disease occurring are inconsistent.