Detection of spinal ataxia in horses using fuzzy clustering of body position uncertainty.
Abstract: 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. Objective: 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 > or = 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: Kinematic gait analysis using simple body marker combinations can be used objectively to detect spinal ataxia in horses.
Publication Date: 2005-01-20 PubMed ID: 15656502DOI: 10.2746/0425164044848163Google Scholar: Lookup
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- Journal Article
- Research Support
- Non-U.S. Gov't
Summary
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This study explores a method to objectively identify spinal ataxia in horses using kinematic data analysis. The method has the potential to reduce bias found in subjective evaluations, and is based on gathering body position data while the horse is walking on a treadmill.
Objective
The research aims to use kinematic data to distinguish between normal horses and those with spinal ataxia. This method has potential benefits in the world of equine practice and research, as it overcomes the limitation of potential biases in subjective neurological examinations in horses.
Methods
- The study involved 24 horses – 12 that were healthy, and 12 that had spinal ataxia.
- All horses were evaluated by means of kinematic analysis as they walked on a treadmill. This involved recording data on their body positions.
- Each body position signal was simplified to a scalar measure of uncertainty – a mathematical value representing uncertainty or randomness.
- These scalar values were then processed using fuzzy clustering, a technique that groups data points that are statistically similar. In this case, the horses were grouped into ‘normal’ or ‘ataxic’ based on their scalar uncertainty values.
- To quantify the effectiveness of this method, the Correct Classification Percentage (CCP) was calculated. This measurement reflects the accuracy of the classification method by showing the ratio of correctly classified horses.
- Further analysis was then performed to discover combinations of measurements that resulted in a high CCP.
Results
- The study identified eight body position measures that delivered a CCP of 70% or higher.
- Several combinations of 4-5 measures resulted in a 100% CCP, indicating perfect accuracy in separating healthy horses from those with spinal ataxia.
- All of the combinations that resulted in 100% CCP could be achieved with one body marker on the horse’s back, measuring vertical and horizontal movement, and one body marker each on the right fore- and hindlimb, measuring vertical movement.
Conclusions
The results indicate that kinematic gait analysis has the potential to detect spinal ataxia in horses in an objective manner. The study identified particular combinations of body markers and movements that resulted in 100% accurate classification. This method could provide a more reliable, less subjective alternative to existing methods of detecting spinal ataxia in horses.
Cite This Article
APA
Keegan KG, Arafat S, Skubic M, Wilson DA, Kramer J, Messer NM, Johnson PJ, O'Brien DP, Johnson G.
(2005).
Detection of spinal ataxia in horses using fuzzy clustering of body position uncertainty.
Equine Vet J, 36(8), 712-717.
https://doi.org/10.2746/0425164044848163 Publication
Researcher Affiliations
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, Missouri 65211, USA.
MeSH Terms
- Algorithms
- Animals
- Biomechanical Phenomena
- Case-Control Studies
- Cluster Analysis
- Exercise Test / veterinary
- Fuzzy Logic
- Gait Ataxia / classification
- Gait Ataxia / diagnosis
- Gait Ataxia / veterinary
- Horse Diseases / classification
- Horse Diseases / diagnosis
- Horses / physiology
Citations
This article has been cited 2 times.- Olsen E, Dunkel B, Barker WH, Finding EJ, Perkins JD, Witte TH, Yates LJ, Andersen PH, Baiker K, Piercy RJ. Rater agreement on gait assessment during neurologic examination of horses.. J Vet Intern Med 2014 Mar-Apr;28(2):630-8.
- Olsen E, Andersen PH, Pfau T. Accuracy and precision of equine gait event detection during walking with limb and trunk mounted inertial sensors.. Sensors (Basel) 2012;12(6):8145-56.
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