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학술지 AI-based Stroke Disease Prediction System Using Real-Time Electromyography Signals
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저자
유재학, 박세진, 권순현, 호치멍 벤자민, 표철식, 이한성
발행일
202010
출처
Applied Sciences, v.10 no.19, pp.1-19
ISSN
2076-3417
출판사
MDPI
DOI
https://dx.doi.org/10.3390/app10196791
초록
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. Inmost cases, patientswith 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.
키워드
Artificial intelligence, Deep learning, Electromyography (EMG), Long short-term memory (LSTM), Machine learning, Random forest, Stroke disease analysis, Stroke prediction
KSP 제안 키워드
Analysis tools, Diagnosis system, Disease prediction, Electromyography (emg), Heart disease, Image Analysis, Long short-term memory, Low-cost, Prediction System, Random forest, Real-Time diagnosis