Comparing Postural Stability Entropy Analyses to Differentiate Fallers and Non-Fallers
| Autor: | Ahmad R. Mojdehi, Khaled Adjerid, Thurmon E. Lockhart, Shane D. Ross, Peter C. Fino, Mohammad Habibi |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Technology and Engineering
Entropy Biomedical Engineering Poison control MULTISCALE APPROXIMATE ENTROPY Logistic regression Multiscale entropy 01 natural sciences Approximate entropy Models Biological Article 010305 fluids & plasmas 03 medical and health sciences Elderly 0302 clinical medicine 0103 physical sciences Statistics Postural Balance Humans OLDER-ADULTS PREDICTORS RQA Simulation Mathematics Aged RISK Aged 80 and over COMPLEXITY RECURRENCE QUANTIFICATION ANALYSIS 030229 sport sciences Sample entropy Fallers Recurrence quantification analysis BALANCE Postural stability Accidental Falls Composite multiscale entropy FOOT PLACEMENT |
| Zdroj: | ANNALS OF BIOMEDICAL ENGINEERING |
| ISSN: | 0090-6964 1573-9686 |
| Popis: | The health and financial cost of falls has spurred research to differentiate the characteristics of fallers and non-fallers. Postural stability has received much of the attention with recent studies exploring various measures of entropy. This study compared the discriminatory ability of several entropy methods at differentiating two paradigms in the center-of-pressure (COP) of elderly individuals: 1.) eyes open (EO) versus eyes closed (EC) and 2.) fallers (F) versus non-fallers (NF). Methods were compared using the area under the curve (AUC) of the receiver-operating characteristic (ROC) curves developed from logistic regression models. Overall, multiscale entropy (MSE) and composite multiscale entropy (CompMSE) performed the best with AUCs of 0.71 for EO/EC and 0.77 for F/NF. When methods were combined together to maximize the AUC, the entropy classifier had an AUC of for 0.91 the F/NF comparison. These results suggest researchers and clinicians attempting to create clinical tests to identify fallers should consider a combination of every entropy method when creating a classifying test. Additionally, MSE and CompMSE classifiers using polar coordinate data outperformed rectangular coordinate data, encouraging more research into the most appropriate time series for postural stability entropy analysis. |
| Databáze: | OpenAIRE |
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