Non-Parametric Functional Muscle Network as a Robust Biomarker of Fatigue

Fri, 20 Jan 2023 00:00:00 +0000

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Abstract

Characterization of fatigue using surface electromyography (sEMG) data has been motivated for rehabilitation and injury-preventative technologies. Current sEMG-based models of fatigue are limited due to (a) linear and parametric assumptions, (b) lack of a holistic neurophysiological view, and (c) complex and heterogeneous responses. This paper proposes and validates a data-driven non-parametric functional muscle network analysis to reliably characterize fatigue-related changes in synergistic muscle coordination and distribution of neural drive at the peripheral level. The proposed approach was tested on data collected in this study from the lower extremities of 26 asymptomatic volunteers (13 subjects were assigned to the fatigue intervention group, and 13 age/gender-matched subjects were assigned to the control group). Volitional fatigue was induced in the intervention group by moderate-intensity unilateral leg press exercises. The proposed non-parametric functional muscle network demonstrated a consistent decrease in connectivity after the fatigue intervention, as indicated by network degree, weighted clustering coefficient (WCC), and global efficiency. The graph metrics displayed consistent and significant decreases at the group level, individual subject level, and individual muscle level. For the first time, this paper proposed a non-parametric functional muscle network and highlighted the corresponding potential as a sensitive biomarker of fatigue with superior performance to conventional spectrotemporal measures.

Muscle fatigue can be characterized using a non-parametric functional muscle network

Citation

@ARTICLE{O-Keeffe2023-is, title = “Non-parametric functional muscle network as a robust biomarker of fatigue”, author = “O’Keeffe, Rory and Shirazi, Seyed Yahya and Yang, Jinghui and Mehrdad, Sarmad and Rao, Smita and Atashzar, S Farokh”, journal = “IEEE J. Biomed. Health Inform.”, publisher = “Institute of Electrical and Electronics Engineers (IEEE)”, volume = “PP”, pages = “1–12”, month = jan, year = 2023, language = “en” }

Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke

Fri, 29 Jul 2022 00:00:00 +0000

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Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions.

Nonlinear functional muscle network for post-stroke sensorimotor integration

Citation

@ARTICLE{OKeeffe2022-uu,
 title = "Nonlinear functional muscle network based on information theory
 tracks sensorimotor integration post stroke",
 author = "O'Keeffe, Rory and Shirazi, Seyed Yahya and Bilaloglu, Seda and
 Jahed, Shayan and Bighamian, Ramin and Raghavan, Preeti and
 Atashzar, S Farokh",
 journal = "Sci. Rep.",
 publisher = "Nature Publishing Group",
 volume = 12,
 number = 1,
 pages = "1--14",
 month = jul,
 year = 2022,
}

Rehabilitation | NINE Lab

Non-Parametric Functional Muscle Network as a Robust Biomarker of Fatigue

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Abstract

Muscle fatigue can be characterized using a non-parametric functional muscle network

Citation

Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke

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Abstract

Nonlinear functional muscle network for post-stroke sensorimotor integration

Citation