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      Classification performance of sEMG and kinematic parameters for distinguishing between non-lame and induced lameness conditions in horses

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          Abstract

          Despite its proven research applications, it remains unknown whether surface electromyography (sEMG) can be used clinically to discriminate non-lame from lame conditions in horses. This study compared the classification performance of sEMG absolute value (sEMGabs) and asymmetry (sEMGasym) parameters, alongside validated kinematic upper-body asymmetry parameters, for distinguishing non-lame from induced fore- (iFL) and hindlimb (iHL) lameness. Bilateral sEMG and 3D-kinematic data were collected from clinically non-lame horses ( n = 8) during in-hand trot. iFL and iHL (2–3/5 AAEP) were induced on separate days using a modified horseshoe, with baseline data initially collected each day. sEMG signals were DC-offset removed, high-pass filtered (40 Hz), and full-wave rectified. Normalized, average rectified value (ARV) was calculated for each muscle and stride (sEMGabs), with the difference between right and left-side ARV representing sEMGasym. Asymmetry parameters (MinDiff, MaxDiff, Hip Hike) were calculated from poll, withers, and pelvis vertical displacement. Receiver-operating-characteristic (ROC) and area under the curve (AUC) analysis determined the accuracy of each parameter for distinguishing baseline from iFL or iHL. Both sEMG parameters performed better for detecting iHL (0.97 ≥ AUC ≥ 0.48) compared to iFL (0.77 ≥ AUC ≥ 0.49). sEMGabs performed better (0.97 ≥ AUC ≥ 0.49) than sEMGasym (0.76 ≥ AUC ≥ 0.48) for detecting both iFL and iHL. Like previous studies, MinDiff Poll and Pelvis asymmetry parameters (MinDiff, MaxDiff, Hip Hike) demonstrated excellent discrimination for iFL and iHL, respectively (AUC > 0.95). Findings support future development of multivariate lameness-detection approaches that combine kinematics and sEMG. This may provide a more comprehensive approach to diagnosis, treatment, and monitoring of equine lameness, by measuring the underlying functional cause(s) at a neuromuscular level.

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          Development of recommendations for SEMG sensors and sensor placement procedures

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            The Use of Surface Electromyography in Biomechanics

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              Receiver operating characteristic curve: overview and practical use for clinicians

              Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and require a process of conversion and interpretation and into a dichotomous form to determine the presence of a disease. The primary method used for this process is the receiver operating characteristic (ROC) curve. The ROC curve is used to assess the overall diagnostic performance of a test and to compare the performance of two or more diagnostic tests. It is also used to select an optimal cut-off value for determining the presence or absence of a disease. Although clinicians who do not have expertise in statistics do not need to understand both the complex mathematical equation and the analytic process of ROC curves, understanding the core concepts of the ROC curve analysis is a prerequisite for the proper use and interpretation of the ROC curve. This review describes the basic concepts for the correct use and interpretation of the ROC curve, including parametric/nonparametric ROC curves, the meaning of the area under the ROC curve (AUC), the partial AUC, methods for selecting the best cut-off value, and the statistical software to use for ROC curve analyses.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/1901741/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role:
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                URI : https://loop.frontiersin.org/people/2060854/overviewRole: Role: Role: Role: Role:
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                URI : https://loop.frontiersin.org/people/2067221/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role:
                Journal
                Front Vet Sci
                Front Vet Sci
                Front. Vet. Sci.
                Frontiers in Veterinary Science
                Frontiers Media S.A.
                2297-1769
                02 April 2024
                2024
                : 11
                : 1358986
                Affiliations
                [1] 1Research Centre for Applied Sport, Physical Activity and Performance, University of Central Lancashire , Preston, United Kingdom
                [2] 2Department of Clinical Sciences, Equine Department, Faculty of Veterinary Medicine, Utrecht University , Utrecht, Netherlands
                [3] 3Department of Large Animal Clinical Sciences, College of Veterinary Medicine, Michigan State University , East Lansing, MI, United States
                [4] 4Delsys/Altec Inc. , Natick, MA, United States
                [5] 5Allied Health Research Unit, University of Central Lancashire , Preston, United Kingdom
                Author notes

                Edited by: Micaela Sgorbini, University of Pisa, Italy

                Reviewed by: Andrea Bertuglia, University of Turin, Italy

                Rebeka R. Zsoldos, The University of Queensland, Australia

                *Correspondence: Tijn J. P. Spoormakers, T.J.P.Spoormakers@ 123456uu.nl
                Article
                10.3389/fvets.2024.1358986
                11018915
                38628939
                24b214a8-992e-4307-895b-e1f52c6221cd
                Copyright © 2024 St. George, Spoormakers, Hobbs, Clayton, Roy, Richards and Serra Bragança.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 20 December 2023
                : 18 March 2024
                Page count
                Figures: 2, Tables: 2, Equations: 0, References: 45, Pages: 10, Words: 7143
                Funding
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was made possible with support from Morris Animal Foundation (Grant ID: D21EQ-406) and the British Society of Animal Science (BSAS) 2018 Steve Bishop Early Career Award. Open Access publication fees were funded by Utrecht University.
                Categories
                Veterinary Science
                Brief Research Report
                Custom metadata
                Comparative and Clinical Medicine

                equine,surface electromyography,gait analysis,roc analysis,movement asymmetry,kinematics,sensitivity,specificity

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