A novel time-based surface EMG measure for quantifying hypertonia in paretic arm muscles during daily activities after hemiparetic stroke
Sohn, M. H.; Deol, J.; Dewald, J. P. A.
Show abstract
After stroke, paretic arm muscles are constantly exposed to abnormal neural drive from the injured brain. As such, hypertonia, broadly defined as an increase in muscle tone, is prevalent especially in distal muscles, which impairs daily function or in long-term leads to a flexed resting posture in the wrist and fingers. However, there currently is no quantitative measure that can reliably track how hypertonia is expressed on daily basis. In this study, we propose a novel time-based surface electromyography (sEMG) measure that can overcome the limitations of the coarse clinical scales often measured in functionally irrelevant context and the magnitude-based sEMG measures that suffer from signal non-stationarity. We postulated that the key to robust quantification of hypertonia is to capture the "true" baseline in sEMG for each measurement session, by which we can define the relative duration of activity over a short time segment continuously tracked in a sliding window fashion. We validate that the proposed measure of sEMG active duration is robust across parameter choices (e.g., sampling rate, window length, threshold criteria), robust against typical noise sources present in paretic muscles (e.g., low signal-to-noise ratio, sporadic motor unit action potentials), and reliable across measurements (e.g., sensors, trials, and days), while providing a continuum of scale over the full magnitude range for each session. Furthermore, sEMG active duration could well characterize the clinically observed differences in hypertonia expressed across different muscles and impairment levels. The proposed measure can be used for continuous and quantitative monitoring of hypertonia during activities of daily living while at home, which will allow for the study of the practical effect of pharmacological and/or physical interventions that try to combat its presence.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Leveraging neural drive to assess hand motor function in individuals with chronic stroke 96%
- Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals 95%
- Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review 93%
Similar papers in this journal
- Toward a generalizable deep CNN for neural drive estimation across muscles and participants 96%
- Identifying alterations in hand movement coordination from chronic stroke survivors using a wearable high-density EMG sleeve 96%
- Adaptive HD-sEMG decomposition: Towards robust real-time decoding of neural drive 96%
Similar papers in this journal
- Autocorrelation-based method to identify disordered rhythm in Parkinsons disease tasks: a novel approach applicable to multimodal devices 95%
- Validation of two-dimensional video-based inference of finger kinematics with pose estimation 94%
- Effect of sampling frequency on fractal fluctuations during treadmill walking 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.