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DOI: https://doi.org/10.14245/ns.1836080.040    [Accepted]
Published online January 4, 2019.
Effective Parameters for Gait Analysis in Experimental Models for Evaluating Peripheral Nerve Injuries in Rats
Ivair Matias Júnior1  , Priscila Medeiros2,3  , Renato Leonardo de Freitas2,4,5  , Hilton Vicente-César6  , José Raniery Ferreira Junior6, Hélio Rubens Machado1, Rafael Menezes-Reis6,7 
1Department of Surgery and Anatomy, Ribeirão Preto Medical School of the University of São Paulo, Ribeirão Preto, Brazil
2Department of Pharmacology, Ribeirão Preto Medical School of the University of São Paulo, Ribeirão Preto, Brazil
3Department of Neuroscience and Behavioural Sciences, Neurology Division, Ribeirão Preto Medical School of the University of São Paulo, Ribeirão Preto, Brazil
4Department of Psychology, School of Philosophy, Science and Literature of Ribeirão Preto of the University of São Paulo, Ribeirão Preto, Brazil
5Biomedical Sciences Institute, Federal University of Alfenas (UNIFAL-MG), Str. Gabriel Monteiro da Silva, Minas Gerais, Brazil
6Center of Imaging Sciences and Medical Physics, Ribeirão Preto Medical School of the University of São Paulo, Ribeirão Preto, Brazil
7Department of Biomechanics, Medicine, and Rehabilitation of Locomotor Apparatus, Ribeirão Preto Medical School of the University of São Paulo, Ribeirão Preto, Brazil
Corresponding Author:  Rafael Menezes-Reis
Tel: +55163315.4690   Fax: +55163315.4690   Email: rafaelmenezesreis@gmail.com
Received: March 14, 2018   Revised: November 23, 2018   Accepted: November 30, 2018
Abstract
Objective
Chronic constriction injury (CCI) of the sciatic nerve is a widely used peripheral nerve injury to induce mononeuropathy. The aim of this study was through the use of machine learning methods, to identify the best parameters in the gait analysis for evaluation of peripheral nerve injury.
Methods
Twenty eight male Wistar rats (weighing 270 ±10g), were used in the present study. Four groups:CCI 4 ligatures of sciatic nerve (CCI-4L; n=7), a modified CCI 1 ligature (CCI-1L; n=7), Sham group (n=7) and a healthy Control group (n=7), were submitted to gait analysis 7 and 28 days after injury. These data were evaluated through softwates Kinovea and WeKa (machine learning and neural networks). Results: Through the machine learning to compare the experimental groups, the parameter Pre-swing angle obtained the highest ranking in all three analyzes (sensitivity, specificity, and area under the ROC curve using the classifiers NB, KNN, and RBF). Initial Contact, Step length and Stride length also performed well. Between the period 7 and 28 days after injury, there was an increase on Total Course Time, Step Length, Stride Length, Stride Speed, Initial Contact and a reduction in Pre-Swing and IC-PS. There was statistically difference between control group and experimental
Keywords: peripheral nerve injury; mononeuropathy; chronic constriction injury; sciatic nerve; motor deficits functions; gait analysis


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