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Assessment of Neuromuscular Function Using Percutaneous Electrical Nerve Stimulation
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A Data-Driven Volitional EMG Extraction Algorithm During Functional Electrical Stimulation With Time Variant

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    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |March 14, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method to extract volitional electromyography (vEMG) signals corrupted by functional electrical stimulation (FES). The SICEEMDAN-LR approach effectively removes FES noise, improving signal clarity for better analysis.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Neuroscience

    Background:

    • Functional electrical stimulation (FES) often contaminates volitional electromyography (vEMG) signals.
    • Extracting clean vEMG during FES is crucial for accurate neuromuscular assessment and control.
    • Existing methods struggle with time-variant FES amplitudes and frequencies.

    Purpose of the Study:

    • To develop a novel algorithm for robust vEMG extraction from FES-contaminated signals.
    • To address the challenge of time-variant FES noise characteristics.
    • To validate the algorithm's performance against established methods and real-world data.

    Main Methods:

    • Selective interpolation (SI) to remove initial stimulation artifacts.
    • Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for signal decomposition.
    • Logistic regression (LR) classifier for identifying and filtering FES-contaminated intrinsic mode functions (IMFs).
    • Validation using semi-simulated and real-world FES-contaminated EMG data.

    Main Results:

    • The proposed SICEEMDAN-LR algorithm demonstrated superior performance in simulations.
    • Achieved higher signal-to-noise ratio (SNR), improved correlation coefficients, and lower normalized root mean squared error (NRMSE) compared to comb filter and EMD-Notch.
    • Successfully extracted vEMG from both able-bodied volunteers and a stroke patient, showing significant EMG-Torque correlation.

    Conclusions:

    • The SICEEMDAN-LR method is effective for extracting vEMG during FES with time-variant parameters.
    • This approach offers a significant improvement for analyzing neuromuscular activity in the presence of FES.
    • The algorithm shows promise for clinical applications, particularly in rehabilitation and neuroprosthetics.