Clinical trial NCT04423497: ESTIMATION OF BALANCE STATUS IN HEMIPARETICS
Official title: ESTIMATION OF BALANCE STATUS IN PATIENTS WITH HEMIPARESIS: AN ARTIFICIAL NEURAL NETWORK IMPLEMENTATION
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Hemiparesis
Keywords: Hemiparesis; Balance; Artificial Neural Networks.
Lead sponsor: Pamukkale University
Sponsor class: OTHER
First posted: 2020-06-09
Enrollment: 66
Sex: ALL
Minimum age (years): 35
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Sixty-six volunteers with hemiparesis (23 females, 43 males) participated in the study. The participants informed of the right to withdraw his or her consent at any time. Prior to giving signed consent, all informed thereof.

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.

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

Eligibility criteria: 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.

Primary outcomes:
measure: Balance Evaluation Systems Test (BESTest)
timeFrame: two years
measure: Artificial Neural Networks Modeling
timeFrame: two years
measure: Artificial Neural Networks Modeling
timeFrame: two years
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: Pamukkale University
name: Güzin Kara, PhD, PT
role: PRINCIPAL_INVESTIGATOR
References:
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citation: 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