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

Yu, Jaehak; Park, Se Jin; Kwon, Soon-Bark; Ho, Chee Meng Benjamin; Pyo, Cheol Sig; Lee, Hansung

doi:10.3390/app10196791

Cited by 66 publications

(23 citation statements)

References 45 publications

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“…Beyond images, various biological signals have also been used to predict stroke diseases. For example, Yu et al [ 39 ] proposed a pre-detection and prediction method for machine learning and deep learning-based stroke diseases that measure the electrical activities of thighs and calves with EMG biological signal sensors, which can easily be used to acquire data during daily activities. They experimentally verified an accuracy of more than 90% using real-time collected data.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

Choi

Park

Jun

et al. 2021

Sensors

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The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and covid-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

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Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

Choi

Park

Jun

et al. 2021

Sensors

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“…A quick literature review found a few studies using various machine-learning techniques, including artificial neural networks (ANN), for stroke diagnosis or prediction [38][39][40][41][42]. For example, Shanthi et al [38] reported that an individual's risk rate for stroke can be detected using ANN based on stroke patient data.…”

Section: Related Workmentioning

confidence: 99%

“…Hanifa et al [41] predicted and verified the risk factors of stroke by adjusting the parameter values of the SVM prediction model using various kernel functions. Yu et al [42] published a study detailing a prior detection and prediction methodology for stroke diseases with machine-learning and deep-learning methodologies by collecting electromyography (EMG) biological signals from thighs and calves in real time. More specifically, they measured and collected EMG data from the left and right thighs and the calves at 1500 Hz from the healthcare device.…”

Section: Related Workmentioning

confidence: 99%

Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals

et al. 2021

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Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (α), beta (β), gamma (γ), delta (δ), and theta (θ) as well as the low β, high β, and θ to β ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future.

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“…Hillaman (2004) researched this type of rehabilitation robotics that assists chronic stroke patients and explained how robotic technology assists in helping people with disabilities. In order to reduce the cost incurred in the initial assessments that take place for a stroke patient, Yu et al (2020) have deployed Machine Learning and Deep Learning algorithms to predict the stroke much earlier by accessing the electromyography signals from humans. The long short-term memory approach is employed to overcome the difficulties faced by the recurrent neural network algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of soft actuators for stroke rehabilitation using deep learning

Alshammari¹

2021

Int. j. adv. appl. sci.

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Automation has created a mind-blowing impact in diversified fields all over the world. Not only in business but also in various domains like health care sectors, manufacturing, etc. a faultless execution is a prime concern. Robotic Process Automation has paved the way for research in the mechanical and mechatronics field. Software robots are trained well to complete repetitive tasks in an efficient manner. A design of such a soft robot can be greatly helpful in the arena of healing. Automation of Rehabilitation therapy has gained attention in recent years. The main aspiration towards the conduct of this research work is to accomplish a soft exoskeleton robot using a thin McKibben actuator applying Deep Learning approaches to aid automatic therapy to the paralyzed patients and help them carry out the hand movement-based exercises. Convolutional Neural Network (CNN) algorithm will be used to support the training of the AI-enabled automated device. The proposed methodology will support stroke survivors to perform exercises independently to enhance their hand motor recovery. For this purpose, it involves pneumatic soft actuator technology using thin McKibben artificial muscles to create a cognitive potential to induce rehabilitation. A soft actuator is proposed so as to confirm the safety purposes of stroke patients.

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

Cited by 66 publications

References 45 publications

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals

Development of soft actuators for stroke rehabilitation using deep learning

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