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Journal of Neurology

, Volume 266, Supplement 1, pp 108–117 | Cite as

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

  • Seyed-Ahmad AhmadiEmail author
  • Gerome Vivar
  • Johann Frei
  • Sergej Nowoshilow
  • Stanislav Bardins
  • Thomas Brandt
  • Siegbert Krafczyk
Original Communication

Abstract

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.

Keywords

Neurological stance and gait disorders Static posturography Body sway Machine learning Visualization 

Abbreviations

ANN

Artificial neural network

AVS

Acute unilateral vestibulopathy

CA

Anterior lobe cerebella atrophy

CI

Confidence interval

DN

Downbeat nystagmus syndrome

kNN

k-Nearest-neighbors

MDI

Mean decrease in impurity

HC

Healthy controls

OT

Primary orthostatic tremor

PCA

Principal component analysis

PD

Parkinson’s disease

PNP

Sensory polyneuropathy

RMS

Root-mean-square

SC

Stacking classifier

SVM

Support vector machine

t-SNE

t-Stochastic neighborhood embedding

Notes

Acknowledgements

The study was supported by the German Federal Ministry of Education and Research (BMBF) in connection with the foundation of the German Center for Vertigo and Balance Disorders (DSGZ) (Grant number 01 EO 0901).

Compliance with ethical standards

Conflicts of interest

None of the authors have potential conflicts of interest to be disclosed.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.German Center for Vertigo and Balance DisordersLudwig Maximilians UniversitätMunichGermany
  2. 2.Computer Aided Medical ProceduresTechnical University of MunichGarchingGermany
  3. 3.IMP Research Institute of Molecular PathologyViennaAustria

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