Next Article in Journal
Performance of Hybrid Filter in a Microgrid Integrated Power System Network Using Wavelet Techniques
Next Article in Special Issue
Prosody-Based Measures for Automatic Severity Assessment of Dysarthric Speech
Previous Article in Journal
T-Spline Surface Toolpath Generation Using Watershed-Based Feature Recognition
Previous Article in Special Issue
Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network
Article

AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals

1
Department of KSB Convergence Research, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
2
School of Computer Engineering, Youngsan University, 288 Junam-Ro, Yangsan, Gyeongnam 50510, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(19), 6791; https://doi.org/10.3390/app10196791
Received: 2 September 2020 / Revised: 24 September 2020 / Accepted: 26 September 2020 / Published: 28 September 2020
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. View Full-Text
Keywords: electromyography (EMG); stroke prediction; stroke disease analysis; artificial intelligence; machine learning; random forest; deep learning; long short-term memory (LSTM) electromyography (EMG); stroke prediction; stroke disease analysis; artificial intelligence; machine learning; random forest; deep learning; long short-term memory (LSTM)
Show Figures

Figure 1

MDPI and ACS Style

Yu, J.; Park, S.; Kwon, S.-H.; Ho, C.M.B.; Pyo, C.-S.; Lee, H. AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals. Appl. Sci. 2020, 10, 6791. https://doi.org/10.3390/app10196791

AMA Style

Yu J, Park S, Kwon S-H, Ho CMB, Pyo C-S, Lee H. AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals. Applied Sciences. 2020; 10(19):6791. https://doi.org/10.3390/app10196791

Chicago/Turabian Style

Yu, Jaehak, Sejin Park, Soon-Hyun Kwon, Chee M.B. Ho, Cheol-Sig Pyo, and Hansung Lee. 2020. "AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals" Applied Sciences 10, no. 19: 6791. https://doi.org/10.3390/app10196791

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop