Automatic Detection of Motor Unit Fractions in Multiscanning EMG Recordings
Mahima Kallingal Muraleedharan1, Cristina Mariscal2, Javier Rodríguez-Falces3
1Universidad Pública de Navarra, Pamplona, Spain. mahima.kallingal@unavarra.es.
Annals of Biomedical Engineering
|February 4, 2026
Summary
This study introduces an automatic algorithm for detecting motor unit (MU) fractions in multiscanning EMG recordings. The novel method reliably identifies MU fractions, enhancing motor unit analysis in clinical and research settings.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Electromyography
Background:
- Motor unit (MU) potential (MUP) scans are crucial for analyzing muscle activity.
- Multiscanning EMG improves efficiency and patient comfort by recording multiple MUPs simultaneously.
- Identifying MU fractions, which represent muscle fiber distribution, is essential for accurate MU analysis.
Purpose of the Study:
- To present a novel algorithm for the automatic detection of MU fractions in MUP scans.
- To evaluate the performance of this algorithm using real-world EMG data.
- To investigate the relationship between muscle depth, MU fraction count, and signal-to-noise ratio (SNR).
Main Methods:
- The algorithm utilizes amplitude thresholding, morphological operations, and connected component analysis.
- Performance was assessed by comparing automatic detection with ground truth data from tibialis anterior muscles.
- Statistical analyses (t-tests, ANOVA) were employed to evaluate accuracy and associations.
Main Results:
- The algorithm demonstrated reliable identification of MU fractions, with no significant difference from ground truth markers.
- No statistically significant effect of muscle depth on signal-to-noise ratio was found (p=0.35).
- The developed algorithm accurately detects MU fractions.
Conclusions:
- The proposed automatic method offers an accurate and valuable tool for MU fraction detection.
- This facilitates improved analysis of motor unit activity in both clinical and research environments.
- The algorithm enhances the efficiency and reliability of EMG data interpretation.
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