Comparison of different algorithms based on TKEO for EMG change point detection

Area of Science:

• Biomedical Engineering
• Signal Processing
• Neuroscience

Background:

• Accurate detection of muscle activation onset/offset in surface electromyography (EMG) is crucial for real-time monitoring.
• The Teager-Kaiser Energy Operator (TKEO) is commonly used for its simplicity and real-time capabilities.
• Limited research exists on the performance of TKEO variants for conditioning EMG signals.

Purpose of the Study:

• To investigate the effectiveness of various energy operators and their rectified versions in detecting changes in EMG signals.
• To compare the stability and accuracy of different energy operators for EMG change point detection.
• To identify optimal methods for improving EMG change point detection performance.

Main Methods:

• Acquired EMG data from extensor carpi radialis longus and flexor carpi radialis muscles of 20 participants.
• Applied four energy operators and their rectified versions for EMG change point detection.
• Evaluated detection performance using standard change points, detection rate, F1 Score, and accuracy.

Main Results:

• The multiresolution energy operator demonstrated high suitability for EMG change point detection.
• The rectified TKEO (abs-TKEO) also showed strong performance in detecting EMG signal changes.
• Both methods proved more effective than other tested operators in this context.

Conclusions:

• The multiresolution energy operator and abs-TKEO are recommended for EMG change point detection.
• This study provides valuable insights for selecting EMG signal conditioning methods.
• Improved EMG change point detection can enhance the performance of muscle activity monitoring systems.