ESTIMATION OF BALANCE STATUS IN HEMIPARETICS

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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Key information

Conditions

Hemiparesis Nervous System Diseases Neurologic Manifestations Paresis Pathological Conditions, Signs and Symptoms Signs and Symptoms

Age range

35 year–65 year

Sex eligibility

All sexes

Study type

Observational

About this study

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

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Being aged between 35-65 years,
Able to walk independently or with a walking aid,
Able to stand at least 1 minute independently,
Having single hemiparesis,
Getting at least 8 points from Hodkinson Mental Test.

Exclusion criteria

Having comorbidities affecting their balance,
Having communication problems.
Patients who cannot comprehend the directions given to them were excluded from the study.

Treatment and study plan

Balance Evaluation Systems Test

Other

Balance Evaluation Systems Test application

Primary outcomes

Balance Evaluation Systems Test (BESTest)

Time frame: two years

Biomechanical constraints, stability limits/verticality, anticipatory postural adjustments, postural responses, sensory orientation and stability in gait
Artificial Neural Networks Modeling

Time frame: two years

comparing the manual total BESTest scores with the predicted scores by the ANNs
Artificial Neural Networks Modeling

Time frame: two years

determining the highest contributions of BESTest subsets in order to find ANNs predictive results of BESTest subsets.

Sponsors and collaborators

Lead sponsor

Pamukkale University

Other

Registry information

Official study title

ESTIMATION OF BALANCE STATUS IN PATIENTS WITH HEMIPARESIS: AN ARTIFICIAL NEURAL NETWORK IMPLEMENTATION

Important dates

Study start: 2016
Primary completion: 2018
Study completion: 2018
First posted: Jun 9, 2020
Registry last updated: Jun 9, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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