2019
DOI: 10.3233/ida-173704
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Classification of patients and controls based on stabilogram signal data

Abstract: Inner ear balance problems are common worldwide and are often difficult to diagnose. In this study we examine the classification of patients with inner ear balance problems and controls (people not suffering from inner ear balance problems) based on data derived from the stabilogram signals and using machine learning algorithms. This paper is a continuation for our earlier paper where the same dataset was used and the focus was medically oriented. Our collected dataset consists of stabilogram (a force platform… Show more

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Cited by 3 publications
(4 citation statements)
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“…We attribute the predictive advantages of the PS score to its ability to detect dynamic patterns of stability and instability (i.e., control failures) and reflect the capability of an individual's postural control system to minimize periods of instability. These measures are beyond the typical linear assessments of posturography and leverage new insights from machine learning and control systems theory ( 19 , 20 ). As time-varying COP is readily available from any laboratory-grade force plate ( 21 ), this implementation of machine learning, which combines linear factors with the detection of primary control failures, can be applied to multiple populations at risk of postural control failure.…”
Section: Discussionmentioning
confidence: 99%
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“…We attribute the predictive advantages of the PS score to its ability to detect dynamic patterns of stability and instability (i.e., control failures) and reflect the capability of an individual's postural control system to minimize periods of instability. These measures are beyond the typical linear assessments of posturography and leverage new insights from machine learning and control systems theory ( 19 , 20 ). As time-varying COP is readily available from any laboratory-grade force plate ( 21 ), this implementation of machine learning, which combines linear factors with the detection of primary control failures, can be applied to multiple populations at risk of postural control failure.…”
Section: Discussionmentioning
confidence: 99%
“…This low-cost force plate was validated to accurately measure center-of-pressure (COP) over time with a frequency of 60 Hz ( 21 ). Using the collected COP data, linear quantifications of postural sway, including: path length, velocity, acceleration and jerk, in both anterior-posterior and medial-lateral directions, as well as non-linear measures of postural stability characterized using a Hidden Markov Model ( 19 , 20 ) were utilized as factors ( 22 ) to calculate the PS score. The PS score is scored ranging from 1 to 10, where larger scores indicate higher stability.…”
Section: Methodsmentioning
confidence: 99%
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“…Custom software (Labview 2011, National Instruments, TX, USA) was used to calculate a score (from 1 to 10) from the COP data, reflecting an estimate of the participant's postural stability and was referred to as a "balance score." Briefly, this custom software collected the COP data then performed linear quantifications of postural sway, including: path length, velocity, acceleration and jerk, in both anterior-posterior and medial-lateral directions, as well as nonlinear measures of postural stability characterized using a Hidden Markov Model (Rasku et al, 2008;Joutsijoki et al, 2019) were utilized as factors (Forth and Lieberman Aiden, 2019) to calculate the balance score. A score of 1 suggested the lowest stability and 10 suggested the highest stability.…”
Section: Methodsmentioning
confidence: 99%