An adapted double threshold protocol for spastic muscles.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Technology

Background:

• Surface electromyography (sEMG) is crucial for understanding motor control and developing medical devices.
• Analyzing spasticity with sEMG is challenging due to involuntary contractions and low signal-to-noise ratio (SNR).
• Existing methods like the double threshold protocol (DTP) fail under non-stationary conditions common in spasticity.

Purpose of the Study:

• To address limitations of current sEMG analysis in spasticity.
• To propose an adapted DTP that accounts for varying noise levels and non-stationary signals.
• To improve the accuracy of muscle contraction detection in spastic individuals.

Main Methods:

• Developed an algorithm to adapt the first threshold of the DTP based on estimated Signal-to-Noise Ratio (SNR).
• Tuned the thresholding to low SNR conditions, where DTP performance degrades most.
• Estimated noise in low-energy segments, avoiding contamination from involuntary contractions.
• Experimentally determined optimal parameters for threshold adaptation.

Main Results:

• The proposed algorithm demonstrated significant improvements in sEMG analysis for spastic muscles.
• Over 23% improvement was observed compared to the classical DTP in moderate to severe spasticity.
• The method was validated on sEMG data from both healthy individuals and a large cohort of spastic patients.

Conclusions:

• The adapted DTP effectively handles the challenges of low SNR and non-stationary signals in spasticity.
• This approach offers a more reliable method for sEMG analysis in clinical settings for spasticity.
• The findings contribute to better understanding and management of motor control disorders.