ESTIMATION OF BALANCE STATUS IN HEMIPARETICS

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

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.

Study Overview

• Status: Completed
• Conditions: Hemiparesis
• Intervention / Treatment: Other: Balance Evaluation Systems Test

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 Type: Observational
• Enrollment (Actual): 66

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 31 years to 61 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• 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.

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Cross-Sectional

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Balance Evaluation Systems Test (BESTest)	Biomechanical constraints, stability limits/verticality, anticipatory postural adjustments, postural responses, sensory orientation and stability in gait	two years
Artificial Neural Networks Modeling	comparing the manual total BESTest scores with the predicted scores by the ANNs	two years
Artificial Neural Networks Modeling	determining the highest contributions of BESTest subsets in order to find ANNs predictive results of BESTest subsets.	two years

Collaborators and Investigators

• Sponsor: Pamukkale University
• Investigators: Principal Investigator: Güzin Kara, PhD, PT, Pamukkale University

Publications and helpful links

General Publications

• 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.
• 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

Study record dates

Study Major Dates

• Study Start (Actual): July 31, 2016
• Primary Completion (Actual): May 31, 2018
• Study Completion (Actual): May 31, 2018

Study Registration Dates

• First Submitted: June 2, 2020
• First Submitted That Met QC Criteria: June 5, 2020
• First Posted (Actual): June 9, 2020

Study Record Updates

• Last Update Posted (Actual): June 9, 2020
• Last Update Submitted That Met QC Criteria: June 5, 2020
• Last Verified: June 1, 2020

More Information

Terms related to this study

• Keywords: Hemiparesis; Balance; Artificial Neural Networks.
• Additional Relevant MeSH Terms: Nervous System Diseases; Neurologic Manifestations; Paresis
• Other Study ID Numbers: 60116787-020/5431

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No