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Updated: Dec 6, 2025
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Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test
Published on: July 27, 2015
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A Method to Differentiate Fatiguing Conditions in Surface Electromyography Signals using Instantaneous Spectral
Summary
A new topological signal processing method quantifies muscle fatigue using surface Electromyography (sEMG) signals. This approach, utilizing Instantaneous Spectral Centroid (ISC), offers a reliable index for muscle fatigue, aiding in diagnosing neuromuscular disorders.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Neuroscience
Background:
- Surface Electromyography (sEMG) signal nonstationarity is a key indicator of muscle fatigue.
- Existing methods for analyzing sEMG fatigue may lack precision or comprehensive analysis.
Purpose of the Study:
- To introduce a novel framework for analyzing sEMG signals using topological signal processing.
- To quantify muscle fatigue nonstationarity with Instantaneous Spectral Centroid (ISC) and other topological features.
Main Methods:
- Recorded sEMG signals from biceps brachii during isometric contraction in 25 healthy subjects.
- Computed analytical signals using Hilbert Transform for nonfatigue and fatigue states.
- Calculated topological features: Center of Gravity (CoG), Triangular Area Function (TAF), and ISC.
Main Results:
- TAF increased, and CoG shifted right in 80% of subjects during fatigue.
- ISC decreased by 17% in 84% of subjects upon fatiguing.
- Topological features demonstrated statistically significant changes (p < 0.05) with muscle fatigue.
Conclusions:
- Topological features effectively quantify nonstationarity in sEMG signals during muscle fatigue.
- The proposed method shows potential as a fatigue index for diagnosing neuromuscular disorders.
- Clinical applications include fitness, sports, and rehabilitation surveillance.
