Artificial neural network: A new diagnostic posturographic tool for disorders of stance

To determine the accuracy of diagnoses made with artificial neural network techniques (ANNW) that identify postural sway patterns typical for balance disorders. Body sway was measured by means of posturography during 10 test conditions of increasing difficulty. From a database of 676 subjects 60 tra...

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Vydáno v:Clinical neurophysiology Ročník 117; číslo 8; s. 1692 - 1698
Hlavní autoři: Krafczyk, Siegbert, Tietze, Simon, Swoboda, Walter, Valkovič, Peter, Brandt, Thomas
Médium: Journal Article
Jazyk:angličtina
Vydáno: Shannon Elsevier Ireland Ltd 01.08.2006
Elsevier Science
Témata:
Adolescent
Adult
Aged
Artificial neural network
Back-propagation-algorithm
Biological and medical sciences
Electrodiagnosis. Electric activity recording
Female
Fundamental and applied biological sciences. Psychology
Humans
Investigative techniques, diagnostic techniques (general aspects)
Male
Medical sciences
Middle Aged
Motor control and motor pathways. Reflexes. Control centers of vegetative functions. Vestibular system and equilibration
Nervous system
Neural Networks (Computer)
Neurological diseases
Postural Balance - physiology
Posturography
Sensation Disorders - diagnosis
Sensitivity and Specificity
Upright stance
Vertebrates: nervous system and sense organs
Neurological diseases
Upright stance
Posturography
Artificial neural network
Back-propagation-algorithm
Human
Validation
Nervous system diseases
Tremor
Anxiety disorder
Neural network
Vertigo
Involuntary movement
Posture
Atrophy
Equilibrium disorder
Phobia
Accuracy
Body movement
Database
ENT disease
Diagnosis
Technique
Neurological disorder
ISSN:1388-2457, 1872-8952
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Popis
Shrnutí:To determine the accuracy of diagnoses made with artificial neural network techniques (ANNW) that identify postural sway patterns typical for balance disorders. Body sway was measured by means of posturography during 10 test conditions of increasing difficulty. From a database of 676 subjects 60 training cases (TCs) and 60 validation cases (VCs) were selected in which the following diagnoses had been established clinically: normal subject (NS), postural phobic vertigo (PPV), anterior lobe cerebellar atrophy (CA), primary orthostatic tremor (OT), and acute unilateral vestibular neuritis (VN). A standard 3-layer feed-forward ANNW, using the backpropagation algorithm, was trained with TCs, validated with VCs, and its accuracy tested on 5 new cases. ANNW differentiated the established diagnoses with an overall sensitivity and specificity of 0.93. Sensitivity and specificity were 1 for NS and OT; for PPV, 0.87 and 0.96; for CA, 1 and 0.98; and for VN, 0.8 and 0.98, respectively. New subjects were identified with ANNW output variables of the true diagnoses between 0.73 and 1. ANNW differentiates postural sway patterns of several distinct clinical balance disorders with high sensitivity and specificity. Once designed and tested ANNW could be considered a black box, which each examiner can apply to predict a specific diagnosis even without a clinical examination. A promising diagnostic tool for disorders of upright stance in selected neurological disorders.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1388-2457
1872-8952
DOI:10.1016/j.clinph.2006.04.022