Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway

We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and data-mining techniques. Body sway is measured in 293 individuals with the clinical diagnoses of acute unilateral vestibulopathy (AVS, n  = 49), distal s...

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Veröffentlicht in:Journal of neurology Jg. 266; H. Suppl 1; S. 108 - 117
Hauptverfasser: Ahmadi, Seyed-Ahmad, Vivar, Gerome, Frei, Johann, Nowoshilow, Sergej, Bardins, Stanislav, Brandt, Thomas, Krafczyk, Siegbert
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019
Springer Nature B.V
Schlagworte:
Adult
Artificial intelligence
Atrophy
Cerebellum
Classification
Cohort Studies
Data Mining - methods
Data processing
Diagnosis, Computer-Assisted - methods
Embedding
Female
Humans
Learning algorithms
Machine Learning
Male
Mapping
Medicine
Medicine & Public Health
Movement disorders
Nervous System Diseases - diagnosis
Nervous System Diseases - physiopathology
Neurodegenerative diseases
Neurological diseases
Neurology
Neuroradiology
Neurosciences
Nystagmus
Original Communication
Parkinson's disease
Polyneuropathy
Postural Balance - physiology
Rubber
Tremor
Vertigo
Visualization
Static posturography
Body sway
Neurological stance and gait disorders
Machine learning
ISSN:0340-5354, 1432-1459, 1432-1459
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Zusammenfassung:We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and data-mining techniques. Body sway is measured in 293 individuals with the clinical diagnoses of acute unilateral vestibulopathy (AVS, n  = 49), distal sensory polyneuropathy (PNP, n  = 12), anterior lobe cerebellar atrophy (CA, n  = 48), downbeat nystagmus syndrome (DN, n  = 16), primary orthostatic tremor (OT, n  = 25), Parkinson’s disease (PD, n  = 27), phobic postural vertigo (PPV n  = 59) and healthy controls (HC, n  = 57). We classify disorders and rank sway features using supervised machine learning. We compute a continuous, human-interpretable 2D map of stance disorders using t-stochastic neighborhood embedding (t-SNE). Classification of eight diagnoses yielded 82.7% accuracy [95% CI (80.9%, 84.5%)]. Five (CA, PPV, AVS, HC, OT) were classified with a mean sensitivity and specificity of 88.4% and 97.1%, while three (PD, PNP, and DN) achieved a mean sensitivity of 53.7%. The most discriminative stance condition was ranked as “standing on foam-rubber, eyes closed”. Mapping of sway path features into 2D space revealed clear clusters among CA, PPV, AVS, HC and OT subjects. We confirm previous claims that machine learning can aid in classification of clinical sway patterns measured with static posturography. Given a standardized, long-term acquisition of quantitative patient databases, modern machine learning and data analysis techniques help in visualizing, understanding and utilizing high-dimensional sensor data from clinical routine.
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ISSN:0340-5354
1432-1459
1432-1459
DOI:10.1007/s00415-019-09458-y