Abstract
Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation.
Highlights
Stroke is one of the leading causes of disability and death in the elderly community [1]
We investigated the myoelectric features of the ischemic stroke group and control group using descriptive statistics to explore the changes in the electrical activity of the muscle
EMG fiducial features ranked higher than the feature importance of 0.95 chosen to train the machine learning (ML) models
Summary
Stroke is one of the leading causes of disability and death in the elderly community [1]. Stroke happens due to brain-cell death in the absence of blood flow to brain cells. Acute ischemic stroke and intracerebral hemorrhage are the leading causes of neurological disorders among the elderly population, and affect millions of people with neurological deficits, physical disabilities, and dependent lifestyles [2,3]. An ischemic lesion affects the functional network architecture of cortical areas and hampers functional motor and cognitive outcomes [4,5,6]. Neurological impairment due to stroke contributes to disability, poor functional improvement, and lower quality of life. The cognitive deficit can reduce the usefulness of post-stroke rehabilitation and vastly increase the risk for psychological disorders such as depression and anxiety. The economic burden of post-stroke treatment of patients with physiological impairment is significantly greater than those without
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
