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

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 ar...

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Bibliographic Details
Published in:Applied sciences 2020-10, Vol.10 (19), p.6791
Main Authors: Yu, Jaehak, Park, Sejin, Kwon, Soon-Hyun, Ho, Chee Meng Benjamin, Pyo, Cheol-Sig, Lee, Hansung
Format: Article
Language:English
Subjects:
Aneurysms
Artificial intelligence
Big Data
Blood clots
Brain research
Calves
Cardiovascular disease
Cardiovascular diseases
Consciousness
Coronary artery disease
Deep learning
Diagnosis
EKG
Electromyography
electromyography (EMG)
Embolisms
Health care policy
Heart
Heart diseases
Hemorrhage
Hypertension
Image analysis
Image processing
Internet of Things
Learning algorithms
Long short-term memory
Machine learning
Magnetic resonance imaging
Personal health
Prediction models
random forest
Real time
Risk factors
Stroke
stroke disease analysis
stroke prediction
Thrombosis
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10196791