Evaluation of a signal-adapted filter for processing of periodic electromyography signals in horses walking on a treadmill.
Abstract: To evaluate an adaptive-filter method for use in analysis of periodic electromyography (EMG) signals in which the transfer function of the filter is matched to characteristics of the signal. Methods: 15 adult horses without clinical signs of back pain. Methods: Electromyography signals of the left and right longissimus dorsi muscles, middle gluteal muscles, and triceps brachii muscle were recorded from horses walking on a treadmill, using bilaterally placed surface electrodes. A reflective marker was placed on the hoof of the left hind limb for simultaneous kinematic measurement of motion cycles. Absolute value of the measured EMG signal was convoluted by use of a filter signal equivalent to the length of 3 motion cycles. The signal-to-noise ratio (SNR) was calculated from the autocorrelation function and compared with the SNR of the unfiltered and the low-pass filtered signals. Results: The signal-adapted filter significantly increased SNR (by 7.3 dB, compared with the low-pass filter, and by 11.1 dB, compared with the unfiltered EMG signal). Conclusions: The signal-adapted filter eliminates signal parts that are not correlated to periodic motion. The method reported here improves the applicability of periodic EMG signals as a clinical tool.
Publication Date: 2001-11-13 PubMed ID: 11703008DOI: 10.2460/ajvr.2001.62.1687Google Scholar: Lookup
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- Evaluation Study
- Journal Article
Summary
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The research article is about how the use of a signal-adapted filter can improve the analysis of periodic electromyography (EMG) signals in horses walking on a treadmill.
Objective and Methodology
The study wanted to test the effectiveness of an adaptive-filter method, which is a filter with transfer functions matched to the characteristics of the EMG signals, in processing and analysing periodic electromyography signals:
- The research team conducted the experiment on 15 healthy adult horses without any signs of back pain.
- They installed surface electrodes bilaterally to record the EMG signals from the left and right longissimus dorsi muscles, middle gluteal muscles, and the triceps brachii muscle of the horses as they walked on a treadmill.
- Along with this, they placed reflective markers on the hoof of the left hind limb of the horses for simultaneous kinematic measurement of motion cycles.
Procedure
- The recorded EMG signal’s absolute value was convoluted using a filter signal of equivalent length to three motion cycles.
- The team calculated the signal-to-noise ratio (SNR) from the autocorrelation function.
- This SNR was then compared with the SNR of the EMG signal that was unfiltered and one that was low-pass filtered.
Results
The results of the study were promising:
- The implementation of the signal-adapted filter showed a significant increase in the SNR by 7.3 dB if compared to the low-pass filter and an increase of 11.1 dB when compared to the unfiltered EMG signal.
Conclusion
Based on the finding of this study, it can be concluded:
- The signal-adapted filter method was beneficial in eliminating signal parts that were not correlated to periodic motion, leading to a much cleaner analysis of the EMG signals.
- This makes the adapted filter method very useful in increasing the applicability of periodic EMG signals as a clinical tool, especially, in this study, for monitoring and analyzing the gait of horses.
Cite This Article
APA
Peham C, Licka TF, Scheidl M.
(2001).
Evaluation of a signal-adapted filter for processing of periodic electromyography signals in horses walking on a treadmill.
Am J Vet Res, 62(11), 1687-1689.
https://doi.org/10.2460/ajvr.2001.62.1687 Publication
Researcher Affiliations
- Clinic for Orthopaedics in Ungulates, University of Veterinary Medicine Vienna, UK.
MeSH Terms
- Animals
- Biomechanical Phenomena
- Electromyography / methods
- Electromyography / veterinary
- Horses / physiology
- Muscle, Skeletal / physiology
- Signal Processing, Computer-Assisted
- Walking / physiology
Citations
This article has been cited 4 times.- Wegscheider J, Lutonsky C, Affenzeller N, Aghapour M, Bockstahler B, Peham C. Determination of the Cutoff Frequency of Smoothing Filters for Center of Pressure (COP) Data via Kinetic Energy in Standing Dogs. Sensors (Basel) 2025 Sep 18;25(18).
- Domino M, Borowska M, Stefanik E, Domańska-Kruppa N, Skibniewski M, Turek B. The Effect of Filtering on Signal Features of Equine sEMG Collected During Overground Locomotion in Basic Gaits. Sensors (Basel) 2025 May 8;25(10).
- Lee HJ, Chang WH, Choi BO, Ryu GH, Kim YH. Age-related differences in muscle co-activation during locomotion and their relationship with gait speed: a pilot study. BMC Geriatr 2017 Jan 31;17(1):44.
- Vögele AM, Zsoldos RR, Krüger B, Licka T. Novel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models. PLoS One 2016;11(6):e0157239.
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