PMID- 31286203
OWN - NLM
STAT- MEDLINE
DCOM- 20200210
LR  - 20200225
IS  - 1432-1459 (Electronic)
IS  - 0340-5354 (Linking)
VI  - 266
IP  - Suppl 1
DP  - 2019 Sep
TI  - Towards computerized diagnosis of neurological stance disorders: data mining and 
      machine learning of posturography and sway.
PG  - 108-117
LID - 10.1007/s00415-019-09458-y [doi]
AB  - 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.
FAU - Ahmadi, Seyed-Ahmad
AU  - Ahmadi SA
AUID- ORCID: 0000-0002-7082-0739
AD  - German Center for Vertigo and Balance Disorders, Ludwig Maximilians Universitat, 
      Marchioninistr. 15, 81377, Munich, Germany. aahmadi@med.lmu.de.
AD  - Computer Aided Medical Procedures, Technical University of Munich, 85748, 
      Garching, Germany. aahmadi@med.lmu.de.
FAU - Vivar, Gerome
AU  - Vivar G
AD  - German Center for Vertigo and Balance Disorders, Ludwig Maximilians Universitat, 
      Marchioninistr. 15, 81377, Munich, Germany.
AD  - Computer Aided Medical Procedures, Technical University of Munich, 85748, 
      Garching, Germany.
FAU - Frei, Johann
AU  - Frei J
AD  - German Center for Vertigo and Balance Disorders, Ludwig Maximilians Universitat, 
      Marchioninistr. 15, 81377, Munich, Germany.
AD  - Computer Aided Medical Procedures, Technical University of Munich, 85748, 
      Garching, Germany.
FAU - Nowoshilow, Sergej
AU  - Nowoshilow S
AD  - IMP Research Institute of Molecular Pathology, Campus-Vienna-Biocenter 1, 1030, 
      Vienna, Austria.
FAU - Bardins, Stanislav
AU  - Bardins S
AD  - German Center for Vertigo and Balance Disorders, Ludwig Maximilians Universitat, 
      Marchioninistr. 15, 81377, Munich, Germany.
FAU - Brandt, Thomas
AU  - Brandt T
AD  - German Center for Vertigo and Balance Disorders, Ludwig Maximilians Universitat, 
      Marchioninistr. 15, 81377, Munich, Germany.
FAU - Krafczyk, Siegbert
AU  - Krafczyk S
AD  - German Center for Vertigo and Balance Disorders, Ludwig Maximilians Universitat, 
      Marchioninistr. 15, 81377, Munich, Germany.
LA  - eng
GR  - 01 EO 0901: German Center for Vertigo and Balance Disorders 
      (DSGZ)/Bundesministerium fur Bildung und Forschung (DE)/
PT  - Journal Article
DEP - 20190708
PL  - Germany
TA  - J Neurol
JT  - Journal of neurology
JID - 0423161
SB  - IM
MH  - Adult
MH  - Cohort Studies
MH  - Data Mining/*methods
MH  - Diagnosis, Computer-Assisted/*methods
MH  - Female
MH  - Humans
MH  - *Machine Learning
MH  - Male
MH  - Nervous System Diseases/*diagnosis/physiopathology
MH  - Postural Balance/*physiology
OTO - NOTNLM
OT  - Body sway
OT  - Machine learning
OT  - Neurological stance and gait disorders
OT  - Static posturography
OT  - Visualization
EDAT- 2019/07/10 06:00
MHDA- 2020/02/11 06:00
CRDT- 2019/07/10 06:00
PHST- 2019/06/11 00:00 [received]
PHST- 2019/06/30 00:00 [accepted]
PHST- 2019/06/28 00:00 [revised]
PHST- 2019/07/10 06:00 [pubmed]
PHST- 2020/02/11 06:00 [medline]
PHST- 2019/07/10 06:00 [entrez]
AID - 10.1007/s00415-019-09458-y [pii]
AID - 10.1007/s00415-019-09458-y [doi]
PST - ppublish
SO  - J Neurol. 2019 Sep;266(Suppl 1):108-117. doi: 10.1007/s00415-019-09458-y. Epub 
      2019 Jul 8.