The effect of cut-off frequency when high-pass filtering equine sEMG signals during locomotion.
Abstract: High-pass filtering (HPF) is a fundamental signal processing method for the attenuation of low-frequency noise contamination, namely baseline noise and movement artefact noise, in human surface electromyography (sEMG) research. Despite this, HPF is largely overlooked in equine sEMG research, with many studies not applying, or failing to describe, the application of HPF. An optimal HPF cut-off frequency maximally attenuates noise while minimally affecting sEMG signal power, but this has not been investigated for equine sEMG signals. The aim of this study was to determine the optimal cut-off frequency for attenuation of low-frequency noise in sEMG signals from the Triceps Brachii and Biceps Femoris of 20 horses during trot and canter. sEMG signals were HPF with cut-off frequencies ranging from 0 to 80 Hz and were subjected to power spectral analysis and enveloped using RMS to calculate spectral peaks, indicative of motion artefact, and signal loss, respectively. Processed signals consistently revealed a low-frequency peak between 0 and 20 Hz, which was associated with motion artefact. Across all muscles and gaits, a 30-40 Hz cut-off fully attenuated the low-frequency peak with the least amount of signal loss and was therefore considered optimal for attenuating low-frequency noise from the sEMG signals explored in this study.
Copyright © 2018 Elsevier Ltd. All rights reserved.
Publication Date: 2018-09-11 PubMed ID: 30219734DOI: 10.1016/j.jelekin.2018.09.001Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
- Journal Article
Summary
This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.
The research paper examines the impact of different high-pass filtering (HPF) cut-off frequencies in reducing low-frequency noise from surface electromyography (sEMG) signals in horses and determines the optimal cut-off frequency for this purpose.
Introduction to High-Pass Filtering and its Application
- High-pass filtering is a critical signal processing method typically used for reducing low-frequency noise contamination, such as baseline noise and movement artefact noise, in human surface electromyography research.
- In the context of electromyography, the goal of the filter is to lower disturbances while affecting the sEMG signal power as minimally as possible.
- This study notes a lack of attention given to high-pass filtering within equine sEMG research, with many studies not detailing or applying HPF. This research will thus focus on determining optimal HPF cut-off frequencies for equine sEMG signals.
Methodology and Data Collection
- The study involved sEMG signals collected from the Triceps Brachii and Biceps Femoris muscles of 20 horses during trot and canter movements.
- These signals were subjected to high-pass filtering with various cut-off frequencies ranging from 0 to 80 Hz. They were then analyzed using power spectral analysis and enveloped using RMS to calculate spectral peaks and signal loss respectively.
Results of the Research
- The processed signals consistently showed a low-frequency peak between 0 and 20 Hz, which was identified as the motion artifact.
- The study found that, across all muscles and movements, a cut-off frequency of 30-40 Hz was optimal in fully attenuating this low-frequency peak while minimising signal loss. This was therefore identified as the optimal cut-off for lowering low-frequency noise in the explored equine sEMG signals.
Conclusion
- This study provides valuable insight that can guide the process of filtering equine sEMG signals, helping maximise the quality of collected data and minimising potential artefacts.
- It highlights the importance of considering signal processing techniques such as high-pass filtering, when conducting surface electromyography studies on equines.
Cite This Article
APA
St George L, Hobbs SJ, Richards J, Sinclair J, Holt D, Roy SH.
(2018).
The effect of cut-off frequency when high-pass filtering equine sEMG signals during locomotion.
J Electromyogr Kinesiol, 43, 28-40.
https://doi.org/10.1016/j.jelekin.2018.09.001 Publication
Researcher Affiliations
- University of Central Lancashire, Centre for Applied Sport and Exercise Sciences, Preston PR1 2HE, United Kingdom. Electronic address: lbstgeorge@uclan.ac.uk.
- University of Central Lancashire, Centre for Applied Sport and Exercise Sciences, Preston PR1 2HE, United Kingdom.
- University of Central Lancashire, Allied Health Research Unit, Preston PR1 2HE, United Kingdom.
- University of Central Lancashire, Centre for Applied Sport and Exercise Sciences, Preston PR1 2HE, United Kingdom.
- Department of Research, Myerscough College, Preston, United Kingdom.
- Delsys Inc., Natick, MA, United States.
MeSH Terms
- Animals
- Electromyography / methods
- Female
- Gait / physiology
- Horses / physiology
- Locomotion / physiology
- Male
- Muscle, Skeletal / physiology
- Signal Processing, Computer-Assisted
Citations
This article has been cited 14 times.- St George L, Spoormakers TJP, Roy SH, Hobbs SJ, Clayton HM, Richards J, Serra Bragança FM. Reliability of surface electromyographic (sEMG) measures of equine axial and appendicular muscles during overground trot. PLoS One 2023;18(7):e0288664.
- Takahashi Y, Takahashi T, Mukai K, Ebisuda Y, Ohmura H. Effect of speed and leading or trailing limbs on surface muscle activities during canter in Thoroughbred horses. PLoS One 2023;18(5):e0286409.
- St George LB, Spoormakers TJP, Smit IH, Hobbs SJ, Clayton HM, Roy SH, van Weeren PR, Richards J, Serra Bragança FM. Adaptations in equine appendicular muscle activity and movement occur during induced fore- and hindlimb lameness: An electromyographic and kinematic evaluation. Front Vet Sci 2022;9:989522.
- Gamucci F, Pallante M, Molle S, Merlo E, Bertuglia A. A Preliminary Study on the Use of HD-sEMG for the Functional Imaging of Equine Superficial Muscle Activation during Dynamic Mobilization Exercises. Animals (Basel) 2022 Mar 20;12(6).
- Rankins EM, Manso Filho HC, Malinowski K, McKeever KH. Muscular tension as an indicator of acute stress in horses. Physiol Rep 2022 Mar;10(6):e15220.
- Busse NI, Gonzalez ML, Krason ML, Johnson SE. β-Hydroxy β-methylbutyrate supplementation to adult Thoroughbred geldings increases type IIA fiber content in the gluteus medius. J Anim Sci 2021 Oct 1;99(10).
- St George L, Clayton HM, Sinclair J, Richards J, Roy SH, Hobbs SJ. Muscle Function and Kinematics during Submaximal Equine Jumping: What Can Objective Outcomes Tell Us about Athletic Performance Indicators?. Animals (Basel) 2021 Feb 5;11(2).
- De la Fuente C, Machado ÁS, Kunzler MR, Carpes FP. Winter School on sEMG Signal Processing: An Initiative to Reduce Educational Gaps and to Promote the Engagement of Physiotherapists and Movement Scientists With Science. Front Neurol 2020;11:509.
- St George L, Nankervis K, Walker V, Maddock C, Robinson A, Sinclair J, Hobbs SJ. A Feasibility Study to Determine Whether Neuromuscular Adaptations to Equine Water Treadmill Exercise Can Be Detected Using Synchronous Surface Electromyography and Kinematic Data. Animals (Basel) 2025 Nov 1;15(21).
- 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).
- Kasnesis P, Plavoukou T, Syropoulou AC, Toumanidis L, Georgoudis G. A Knee Rehabilitation Exercises Dataset for Postural Assessment using Wearable Devices. Sci Data 2025 Apr 11;12(1):610.
- Takahashi Y, Takahashi T, Mukai K, Ebisuda Y, Ohmura H. Changes in muscle activation with graded surfaces during canter in Thoroughbred horses on a treadmill. PLoS One 2024;19(6):e0305622.
- St George LB, Spoormakers TJP, Hobbs SJ, Clayton HM, Roy SH, Richards J, Serra Bragança FM. Classification performance of sEMG and kinematic parameters for distinguishing between non-lame and induced lameness conditions in horses. Front Vet Sci 2024;11:1358986.
- St George LB, Clayton HM, Sinclair JK, Richards J, Roy SH, Hobbs SJ. Electromyographic and Kinematic Comparison of the Leading and Trailing Fore- and Hindlimbs of Horses during Canter. Animals (Basel) 2023 May 25;13(11).
Use Nutrition Calculator
Check if your horse's diet meets their nutrition requirements with our easy-to-use tool Check your horse's diet with our easy-to-use tool
Talk to a Nutritionist
Discuss your horse's feeding plan with our experts over a free phone consultation Discuss your horse's diet over a phone consultation
Submit Diet Evaluation
Get a customized feeding plan for your horse formulated by our equine nutritionists Get a custom feeding plan formulated by our nutritionists
