A pseudojoint estimation of time delay and scale factor for M-wave analysis

Area of Science:

• Signal Processing
• Biomedical Engineering
• Statistical Signal Analysis

Background:

• Accurate estimation of time scale and time delay is crucial for analyzing transient signals.
• Dependent parameters like time scale and delay often complicate joint estimation methods.
• Existing methods may suffer from bias or computational complexity.

Purpose of the Study:

• To develop a pseudojoint estimation method for time scale and time delay of transient signals.
• To enable independent estimation of time scale and time delay for improved accuracy.
• To apply the method to electromyogram (EMG) signal analysis for muscle fatigue detection.

Main Methods:

• Utilizes the time autocorrelation function (TACF) to preserve time scale information.
• Separates the estimation of time scale and time delay.
• Employs scale transforms in the scale domain for high-resolution time scale estimation.
• Applies signal scaling post-time scale estimation to remove bias in time delay estimation.

Main Results:

• The proposed method achieves independent estimation of time scale and time delay.
• Demonstrates good performance comparable to Cramér-Rao Lower Bound and Maximum Likelihood Estimation on synthetic data.
• Time scale estimation effectively reveals myoelectric manifestations of muscle fatigue.
• The method shows robustness against M-wave truncation in EMG signals.

Conclusions:

• The pseudojoint estimation method provides an efficient and accurate approach for analyzing transient signals.
• Independent estimation of time scale and time delay enhances signal analysis reliability.
• The technique offers a valuable tool for non-invasive monitoring of muscle fatigue through EMG analysis.