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    A new cumulative histogram filtering (CHF) algorithm effectively removes impulsive artifacts from surface electromyography (sEMG) signals. This method preserves signal integrity for accurate time-domain feature extraction in real-time applications.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Rehabilitation Engineering

    Background:

    • Surface electromyography (sEMG) signals are crucial for understanding muscle activity.
    • Impulsive artifacts frequently contaminate sEMG data, hindering accurate feature extraction.
    • Existing filtering methods may struggle with real-time processing and artifact suppression.

    Purpose of the Study:

    • To introduce a novel cumulative histogram filtering (CHF) algorithm for sEMG artifact removal.
    • To develop an efficient, iterative implementation of the CHF algorithm for embedded systems.
    • To evaluate the effectiveness of the CHF algorithm in suppressing impulsive artifacts while preserving signal quality.

    Main Methods:

    • Developed a cumulative histogram filtering (CHF) algorithm based on probabilistic amplitude distribution within a sliding window.
    • Created an efficient, iterative implementation of the CHF algorithm for real-time processing.
    • Superimposed synthetic impulse artifacts onto clean sEMG signals from a transtibial amputee subject for testing.

    Main Results:

    • The CHF algorithm demonstrated effective suppression of simulated impulsive artifacts.
    • Preserved a minimum signal-to-noise ratio of 95% after artifact removal.
    • Achieved an average Pearson correlation of 0.99 between filtered and undisturbed sEMG signals.

    Conclusions:

    • The proposed CHF algorithm is a robust and efficient method for filtering impulsive artifacts in sEMG signals.
    • The algorithm is suitable for real-time applications on embedded platforms.
    • CHF effectively maintains sEMG signal fidelity for reliable time-domain feature extraction.