ESTIMATION OF BALANCE STATUS IN HEMIPARETICS
Condition: Hemiparesis
MeSH terms: Paresis
Study type: Observational

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
    

Intervention name(s): Balance Evaluation Systems Test
Intervention type(s): Other
Intervention description(s): Balance Evaluation Systems Test application
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.
      
Keywords: Hemiparesis; Balance; Artificial Neural Networks.