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Detection of spinal ataxia in horses using fuzzy clustering of body position uncertainty

Summary Reasons for performing study: Subjective neurological evaluation in horses is prone to bias. An objective method of spinal ataxia detection is not subject to these limitations and could be of use in equine practice and research. Hypothesis: Kinematic data in the walking horse can differentia...

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Published in:Equine veterinary journal 2004-12, Vol.36 (8), p.712-717
Main Authors: Keegan, K.G, Arafat, S, Skubic, M, Wilson, D.A, Kramer, J, Messer, N.M, Johnson, P.J, O'Brien, D.P, Johnson, G
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cited_by cdi_FETCH-LOGICAL-c4045-f7f6ad4f6d89433374909444ec09522f649a054f279b63dad6d32838a77338873
cites cdi_FETCH-LOGICAL-c4045-f7f6ad4f6d89433374909444ec09522f649a054f279b63dad6d32838a77338873
container_end_page 717
container_issue 8
container_start_page 712
container_title Equine veterinary journal
container_volume 36
creator Keegan, K.G
Arafat, S
Skubic, M
Wilson, D.A
Kramer, J
Messer, N.M
Johnson, P.J
O'Brien, D.P
Johnson, G
description Summary Reasons for performing study: Subjective neurological evaluation in horses is prone to bias. An objective method of spinal ataxia detection is not subject to these limitations and could be of use in equine practice and research. Hypothesis: Kinematic data in the walking horse can differentiate normal and spinal ataxic horses. Methods: Twelve normal and 12 spinal ataxic horses were evaluated by kinematic analysis walking on a treadmill. Each body position signal was reduced to a scalar measure of uncertainty then fuzzy clustered into normal or ataxic groups. Correct classification percentage (CCP) was then calculated using membership values of each horse in the 2 groups. Subsequently, a guided search for measure combinations with high CCP was performed. Results: Eight measures of body position resulted in CCP≥70%. Several combinations of 4–5 measures resulted in 100% CCP. All combinations with 100% CCP could be obtained with one body marker on the back measuring vertical and horizontal movement and one body marker each on the right fore‐ and hindlimb measuring vertical movement. Conclusions and potential relevance: Kinematic gait analysis using simple body marker combinations can be used objectively to detect spinal ataxia in horses.
doi_str_mv 10.2746/0425164044848163
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An objective method of spinal ataxia detection is not subject to these limitations and could be of use in equine practice and research. Hypothesis: Kinematic data in the walking horse can differentiate normal and spinal ataxic horses. Methods: Twelve normal and 12 spinal ataxic horses were evaluated by kinematic analysis walking on a treadmill. Each body position signal was reduced to a scalar measure of uncertainty then fuzzy clustered into normal or ataxic groups. Correct classification percentage (CCP) was then calculated using membership values of each horse in the 2 groups. Subsequently, a guided search for measure combinations with high CCP was performed. Results: Eight measures of body position resulted in CCP≥70%. Several combinations of 4–5 measures resulted in 100% CCP. All combinations with 100% CCP could be obtained with one body marker on the back measuring vertical and horizontal movement and one body marker each on the right fore‐ and hindlimb measuring vertical movement. Conclusions and potential relevance: Kinematic gait analysis using simple body marker combinations can be used objectively to detect spinal ataxia in horses.</description><identifier>ISSN: 0425-1644</identifier><identifier>EISSN: 2042-3306</identifier><identifier>DOI: 10.2746/0425164044848163</identifier><identifier>PMID: 15656502</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Animals ; ataxia (disorder) ; Biomechanical Phenomena ; Case-Control Studies ; classification ; Cluster Analysis ; disease detection ; exercise test ; Exercise Test - veterinary ; fuzzy c-means ; Fuzzy Logic ; gait ; Gait Ataxia - classification ; Gait Ataxia - diagnosis ; Gait Ataxia - veterinary ; horse ; horse diseases ; Horse Diseases - classification ; Horse Diseases - diagnosis ; horses ; Horses - physiology ; kinematics ; signal uncertainty ; spinal ataxia ; spine ; walking</subject><ispartof>Equine veterinary journal, 2004-12, Vol.36 (8), p.712-717</ispartof><rights>2004 EVJ Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4045-f7f6ad4f6d89433374909444ec09522f649a054f279b63dad6d32838a77338873</citedby><cites>FETCH-LOGICAL-c4045-f7f6ad4f6d89433374909444ec09522f649a054f279b63dad6d32838a77338873</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27922,27923</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15656502$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Keegan, K.G</creatorcontrib><creatorcontrib>Arafat, S</creatorcontrib><creatorcontrib>Skubic, M</creatorcontrib><creatorcontrib>Wilson, D.A</creatorcontrib><creatorcontrib>Kramer, J</creatorcontrib><creatorcontrib>Messer, N.M</creatorcontrib><creatorcontrib>Johnson, P.J</creatorcontrib><creatorcontrib>O'Brien, D.P</creatorcontrib><creatorcontrib>Johnson, G</creatorcontrib><title>Detection of spinal ataxia in horses using fuzzy clustering of body position uncertainty</title><title>Equine veterinary journal</title><addtitle>Equine Vet J</addtitle><description>Summary Reasons for performing study: Subjective neurological evaluation in horses is prone to bias. An objective method of spinal ataxia detection is not subject to these limitations and could be of use in equine practice and research. Hypothesis: Kinematic data in the walking horse can differentiate normal and spinal ataxic horses. Methods: Twelve normal and 12 spinal ataxic horses were evaluated by kinematic analysis walking on a treadmill. Each body position signal was reduced to a scalar measure of uncertainty then fuzzy clustered into normal or ataxic groups. Correct classification percentage (CCP) was then calculated using membership values of each horse in the 2 groups. Subsequently, a guided search for measure combinations with high CCP was performed. Results: Eight measures of body position resulted in CCP≥70%. Several combinations of 4–5 measures resulted in 100% CCP. All combinations with 100% CCP could be obtained with one body marker on the back measuring vertical and horizontal movement and one body marker each on the right fore‐ and hindlimb measuring vertical movement. 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An objective method of spinal ataxia detection is not subject to these limitations and could be of use in equine practice and research. Hypothesis: Kinematic data in the walking horse can differentiate normal and spinal ataxic horses. Methods: Twelve normal and 12 spinal ataxic horses were evaluated by kinematic analysis walking on a treadmill. Each body position signal was reduced to a scalar measure of uncertainty then fuzzy clustered into normal or ataxic groups. Correct classification percentage (CCP) was then calculated using membership values of each horse in the 2 groups. Subsequently, a guided search for measure combinations with high CCP was performed. Results: Eight measures of body position resulted in CCP≥70%. Several combinations of 4–5 measures resulted in 100% CCP. 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subjects Algorithms
Animals
ataxia (disorder)
Biomechanical Phenomena
Case-Control Studies
classification
Cluster Analysis
disease detection
exercise test
Exercise Test - veterinary
fuzzy c-means
Fuzzy Logic
gait
Gait Ataxia - classification
Gait Ataxia - diagnosis
Gait Ataxia - veterinary
horse
horse diseases
Horse Diseases - classification
Horse Diseases - diagnosis
horses
Horses - physiology
kinematics
signal uncertainty
spinal ataxia
spine
walking
title Detection of spinal ataxia in horses using fuzzy clustering of body position uncertainty
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