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Sensors (Basel, Switzerland)2025; 25(10); doi: 10.3390/s25102962

The Effect of Filtering on Signal Features of Equine sEMG Collected During Overground Locomotion in Basic Gaits.

Abstract: In equine surface electromyography (sEMG), challenges related to the reliability and interpretability of data arise, among other factors, from methodological differences, including signal processing and analysis. The aim of this study is to demonstrate the filtering-induced changes in basic signal features in relation to the balance between signal loss and noise attenuation. Raw sEMG signals were collected from the quadriceps muscle of six horses during walk, trot, and canter and then filtered using eight filtering methods with varying cut-off frequencies (low-pass at 10 Hz, high-pass at 20 Hz and 40 Hz, and bandpass at 20-450 Hz, 40-450 Hz, 7-200 Hz, 15-500 Hz, and 30-500 Hz). For each signal variation, signal features-such as amplitude, root mean square (RMS), integrated electromyography (iEMG), median frequency (MF), and signal-to-noise ratio (SNR)-along with signal loss metrics and power spectral density (PSD), were calculated. High-pass filtering at 40 Hz and bandpass filtering at 40-450 Hz introduced significant filtering-induced changes in signal features while providing full attenuation of low-frequency noise contamination, with no observed differences in signal loss between these two methods. Other filtering methods led to only partial attenuation of low-frequency noise, resulting in lower signal loss and less consistent changes across gaits in signal features. Therefore, filtering-induced changes should be carefully considered when comparing signal features from studies using different filtering approaches. These findings may support cross-referencing in equine sEMG research related to training, rehabilitation programs, and the diagnosis of musculoskeletal diseases, and emphasize the importance of applying standardized filtering methods, particularly with a high-pass cut-off frequency set at 40 Hz.
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Publication Date: 2025-05-08 PubMed ID: 40431757PubMed Central: PMC12115114DOI: 10.3390/s25102962Google Scholar: Lookup
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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.

This research paper evaluates how varying methods of signal processing impact the reliability and interpretability of data when studying horse locomotion using surface electromyography (sEMG). With a focus on the balance between signal loss and noise reduction, the authors uncover which filtering methods present the most trustworthy results.

Study Aim and Methodology

The primary goal of this research was to shed light on how different filtering methods impact the basic signal features when studying horse locomotion via sEMG.

  • This study used raw sEMG signals collected from the quadriceps muscle of six horses performing various basic locomotive activities – walking, trotting, and cantering.
  • The signals were then filtered through eight different methods with varying cut-off frequencies. These methods include both high-pass and low-pass filtering, plus bandpass filtering covering a range of frequencies.
  • Post signal filtration, various signal features like amplitude, root mean square (RMS), integrated electromyography (iEMG), median frequency (MF), and signal-to-noise ratio (SNR) were calculated along with signal loss metrics and power spectral density (PSD) for each signal variation.

Key Findings

The research discovered significant filtering-induced changes in signal features based on the filtering method used.

  • A high-pass filtering set at 40 Hz, and a bandpass filtering set at 40-450 Hz contributed to significant changes in signal features but facilitated complete attenuation of low-frequency noise.
  • Interestingly, these two methods did not result in differing degrees of signal loss.
  • Other filtering methods led to only partial reduction of low-frequency noise contamination and resulted in lower signal loss. However, they also led to less consistent signal features changes across different gaits.

Implications and Recommendations

The results of the study underline the importance of carefully considering the method of signal filtration when comparing sEMG studies of equine locomotion and drawing reliable inferences.

  • It emphasizes the significance of adopting standardized filtering methods, particularly recommending a high-pass cut-off frequency set at 40 Hz.
  • This research is beneficial for equine sEMG studies used for training, rehabilitation programs, and diagnosing musculoskeletal diseases in horses.
  • It can help in creating a more reliable cross-referencing system among sEMG studies involving different filtering approaches.

Cite This Article

APA
Domino M, Borowska M, Stefanik E, Domańska-Kruppa N, Skibniewski M, Turek B. (2025). The Effect of Filtering on Signal Features of Equine sEMG Collected During Overground Locomotion in Basic Gaits. Sensors (Basel), 25(10). https://doi.org/10.3390/s25102962

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 25
Issue: 10

Researcher Affiliations

Domino, Małgorzata
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Borowska, Marta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Stefanik, Elżbieta
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Domańska-Kruppa, Natalia
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Skibniewski, Michał
  • Department of Morphological Sciences, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS-SGGW), 02-787 Warsaw, Poland.
Turek, Bernard
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.

MeSH Terms

  • Animals
  • Electromyography / methods
  • Horses / physiology
  • Signal Processing, Computer-Assisted
  • Signal-To-Noise Ratio
  • Gait / physiology
  • Locomotion / physiology

Grant Funding

  • POIR.01.01.01-00-1001/20 / National Centre for Research and Development

Conflict of Interest Statement

The authors declare no conflicts of interest.

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