ESTIMATION OF BALANCE STATUS IN HEMIPARETICS
Study Details
Study Description
Brief Summary
Although Balance Evaluation Systems Test(BESTest) is an important balance assessment tool to differentiate balance deficits, it is time consuming and tiring for hemiparetic patients. Using artificial neural networks(ANNs) to estimate balance status can be a practical and useful tool for clinicians. The aim of this study was to compare manual BESTest results and ANNs predictive results and to determine the highest contributions of BESTest sections by using ANNs predictive results of BESTest sections. 66 hemiparetic individuals were included in the study. Balance status was evaluated using the BESTest. 70%(n=46), of the dataset was used for learning, 15%(n=10) for evaluation, and 15%(n=10) for testing purposes in order to model ANNs. Multiple linear regression model(MLR) was used to compare with ANNs.
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Detailed Description
The demographics and clinical information of the participants' were recorded. Clinical information consists of some basic medical data for the patients. Hodkinson Mental Test was used to assess the cognitive status of the participants if they met inclusion criteria. Balance Evaluation Systems Test was used to assess balance status of the participants.
Feed-forward back-propagation ANNs was used in this study by employing Levenberg-Marquardt training algorithm. Tangent hyperbolic transfer functions were used in the hidden layer. Matlab (Version R2017b, Mathworks Inc, USA) was used in ANNs modeling. 70% (n=46), 15% (n=10) and 15% (n=10) of the data obtained from the participants were used for training, validation and test in the study, respectively. Multiple linear regression (MLR) models also were used to compare with ANNs.
Firstly, the ANNs were modeled for the first aim of the study. We used the data of the five traditional balance tests in the BESTest that did not use the real values (the timing or distance), but just the classified values (0-3 points in the BESTest) to train ANNs. Five balance tests were functional reach test (cm), one leg standing test for right and left side (sec), 6-metre timed walk test (sec) and timed up and go test (sec). Then, we compare the manual total BESTest scores with the predicted scores by the ANNs.
Secondly, we removed 6 sections of the BESTest one by one and modeled with the remaining 5 sections of the test to estimate the total BESTest score. After this modeling, we removed each item one by one in the first section and estimated the first section total score. We repeated the process for all the sections of the BESTest.
Statistical Analysis
Study Design
Outcome Measures
Primary Outcome Measures
- Balance Evaluation Systems Test (BESTest) [two years]
Biomechanical constraints, stability limits/verticality, anticipatory postural adjustments, postural responses, sensory orientation and stability in gait
- Artificial Neural Networks Modeling [two years]
comparing the manual total BESTest scores with the predicted scores by the ANNs
- Artificial Neural Networks Modeling [two years]
determining the highest contributions of BESTest subsets in order to find ANNs predictive results of BESTest subsets.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Being aged between 35-65 years,
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Able to walk independently or with a walking aid,
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Able to stand at least 1 minute independently,
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Having single hemiparesis,
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Getting at least 8 points from Hodkinson Mental Test.
Exclusion Criteria:
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Having comorbidities affecting their balance,
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Having communication problems.
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Patients who cannot comprehend the directions given to them were excluded from the study.
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Pamukkale University
Investigators
- Principal Investigator: Güzin Kara, PhD, PT, Pamukkale University
Study Documents (Full-Text)
None provided.More Information
Publications
- Demir U, Kocaoğlu S, Akdoğan E. Human impedance parameter estimation using artificial neural network for modelling physiotherapist motion. Biocybernetics and Biomedical Engineering. 2016; 36(2): 318-326
- Kaczmarczyk K, Wit A, Krawczyk M, Zaborski J, Gajewski J. Associations between gait patterns, brain lesion factors and functional recovery in stroke patients. Gait Posture. 2012 Feb;35(2):214-7. doi: 10.1016/j.gaitpost.2011.09.009. Epub 2011 Sep 19.
- 60116787-020/5431