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

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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.

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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

References

  1. Dieterich M, Brandt T (2015) The bilateral central vestibular system: Its pathways, functions, and disorders. Ann N Y Acad Sci. 1343:10–26

    Article  PubMed  Google Scholar 

  2. Brandt T, Dieterich M (2017) The dizzy patient: don’t forget disorders of the central vestibular system. Nat Rev Neurol. 13:352–362

    Article  PubMed  Google Scholar 

  3. Staab JP, Eckhardt-Henn A, Horii A, Jacob R, Strupp M, Brandt T et al (2017) Diagnostic criteria for persistent postural-perceptual dizziness (PPPD): consensus document of the committee for the classification of vestibular disorders of the Bárány Society. J Vestib Res. 27:191–208

    Article  PubMed  Google Scholar 

  4. Black FO (2001) What can posturography tell us about vestibular function? Ann N Y Acad Sci. 942:446–464

    Article  CAS  PubMed  Google Scholar 

  5. de Waele C. Vestibular tests: static and dynamic posturography BT—encyclopedia of neuroscience. In: Binder MD, Hirokawa N, Windhorst U (eds) Springer, Berlin. 2009;4217–8.

  6. Monsell EM, Furman JM, Herdman SJ, Konrad HR, Shepard NT (1997) Computerized dynamic platform posturography. Otolaryngol Head Neck Surg 117:394–398

    Article  CAS  PubMed  Google Scholar 

  7. Krafczyk S, Tietze S, Swoboda W, Valkovi P, Brandt T (2006) Artificial neural network: a new diagnostic posturographic tool for disorders of stance. Clin Neurophysiol. 117:1692–1698

    Article  PubMed  Google Scholar 

  8. Fioretti S, Scocco M, Ladislao L, Ghetti G, Rabini RA (2010) Identification of peripheral neuropathy in type-2 diabetic subjects by static posturography and linear discriminant analysis. Gait Posture. 32:317–320

    Article  CAS  PubMed  Google Scholar 

  9. Howcroft J, Lemaire ED, Kofman J, McIlroy WE (2017) Elderly fall risk prediction using static posturography. PLoS One 12:1–13

    Article  CAS  Google Scholar 

  10. Saripalle SK, Paiva GC, Derakhshani RR, King GW, Lovelace CT (2014) Classification of body movements based on posturographic data. Hum Mov Sci. 33:238–250

    Article  PubMed  Google Scholar 

  11. Vonk J, Horlings CGC, Allum JHJ (2010) Differentiating malingering balance disorder patients from healthy controls, compensated unilateral vestibular loss, and whiplash patients using stance and gait posturography. Audiol Neurotol. 15:261–272

    Article  Google Scholar 

  12. Redfern MS, Furman JM (1994) Postural sway of patients with vestibular disorders during optic flow. J Vestib Res. 4:221–230

    CAS  PubMed  Google Scholar 

  13. Diener HC, Dichgans J, Bacher M, Gompf B (1984) Quantification of postural sway in normals and patients with cerebellar diseases. Clin Neurophysiol. Elsevier 57:134–142

    Article  CAS  Google Scholar 

  14. Helmchen C, Kirchhoff J-B, Göttlich M, Sprenger A (2017) Postural ataxia in cerebellar downbeat nystagmus: its relation to visual, proprioceptive and vestibular signals and cerebellar atrophy. PLoS One 12:e0168808

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Rigby HB, Rigby MH, Caviness JN (2015) Orthostatic tremor: a spectrum of fast and slow frequencies or distinct entities? Tremor Other Hyperkinet Mov (N Y). 5:324

    Google Scholar 

  16. Schöberl F, Feil K, Xiong G, Bartenstein P, la Fougére C, Jahn K et al (2017) Pathological ponto-cerebello-thalamo-cortical activations in primary orthostatic tremor during lying and stance. Brain 140:83–97

    Article  PubMed  Google Scholar 

  17. Ickenstein GW, Ambach H, Klöditz A, Koch H, Isenmann S, Reichmann H et al (2012) Static posturography in aging and Parkinson’s disease. Front Aging Neurosci. 4:20

    Article  PubMed  PubMed Central  Google Scholar 

  18. Brandt T, Strupp M, Novozhilov S, Krafczyk S. Artificial neural network posturography detects the transition of vestibular neuritis to phobic postural vertigo. J. Neurol. 2012. p. 182–4.

  19. Allum JHJ, Bloem BR, Carpenter MG, Honegger F (2001) Differential diagnosis of proprioceptive and vestibular deficits using dynamic support-surface posturography. Gait Posture. 14:217–226

    Article  CAS  PubMed  Google Scholar 

  20. Schwesig R, Fischer D, Becker S, Lauenroth A (2014) Intraobserver reliability of posturography in patients with vestibular neuritis. Somatosens Mot Res. 31:28–34

    Article  PubMed  Google Scholar 

  21. Bronstein AM, Guerraz M (1999) Visual-vestibular control of posture and gait: physiological mechanisms and disorders. Curr Opin Neurol. 12:5–11

    Article  CAS  PubMed  Google Scholar 

  22. Deuschl G, Bain P, Brin M (1998) Consensus statement of the movement disorder society on tremor. Ad Hoc Scientific Committee. Mov Disord. 13:2–23

    Article  Google Scholar 

  23. Strupp M, Hufner K, Sandmann R, Zwergal A, Dieterich M, Jahn K et al (2011) Central oculomotor disturbances and nystagmus: a window into the brainstem and cerebellum. Dtsch Arztebl Int. 108:197–204

    PubMed  PubMed Central  Google Scholar 

  24. Strupp M, Brandt T (1999) Vestibular neuritis. Adv Otorhinolaryngol. 55:111–136

    CAS  PubMed  Google Scholar 

  25. Gelb DJ, Oliver E, Gilman S (1999) Diagnostic criteria for Parkinson disease. Arch Neurol. 56:33–39

    Article  CAS  PubMed  Google Scholar 

  26. Richardson JK, Thies S, Ashton-Miller JA (2008) An exploration of step time variability on smooth and irregular surfaces in older persons with neuropathy. Clin Biomech. 23:349–356

    Article  Google Scholar 

  27. Brandt T (1996) Phobic postural vertigo. Neurology. 46:1515–1519

    Article  CAS  PubMed  Google Scholar 

  28. Hufschmidt A, Dichgans J, Mauritz K-H, Hufschmidt M (1980) Some methods and parameters of body sway quantification and their neurological applications. Arch Psychiatr Nervenkr. 228:135–150

    Article  CAS  PubMed  Google Scholar 

  29. Brandt T, Krafczyk S, Malsbenden I (1981) Postural imbalance with head extension: improvement by training as a model for ataxia therapy. Ann N Y Acad Sci. 374:636–649

    Article  CAS  PubMed  Google Scholar 

  30. Caruana R, Karampatziakis N, Yessenalina A. An empirical evaluation of supervised learning in high dimensions. Proc 25th Int Conf Mach Learn-ICML ’08. 2008. p. 96–103.

  31. Yu H-F, Huang F-L, Lin C-J (2011) Dual coordinate descent methods for logistic regression and maximum entropy models. Mach Learn. 85:41–75

    Article  Google Scholar 

  32. Altman NS (1992) An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. Am Stat. 46:175–185

    Google Scholar 

  33. Hinton GE (1989) Connectionist learning procedures. Artif Intell. 40:185–234

    Article  Google Scholar 

  34. Smola AJ, Sch B, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput. 14:199–222

    Article  Google Scholar 

  35. Criminisi, A, Konukoglu E, Shotton J (2011) Decision forests for classification, regression, density estimation manifold learning and semi-supervised learning microsoft technical report

  36. Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn. 63:3–42

    Article  Google Scholar 

  37. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res. 12:2825–2830

    Google Scholar 

  38. Wolpert DH (1992) Stacked generalization. Neural Netw. 5:241–259

    Article  Google Scholar 

  39. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. ed. 1. Wadsworth Stat. Ser. Chapman and Hall/CRC; 1984.

  40. van der Maaten L, Hinton GE (2008) Visualizing high-dimensional data using t-SNE. J Mach Learn Res. 9:2579–2605

    Google Scholar 

  41. Jones E, Oliphant T, Peterson P, others. SciPy: Open source scientific tools for Python [Internet]. 2001. https://www.scipy.org/

  42. Gallea C, Popa T, García-Lorenzo D, Valabregue R, Legrand AP, Apartis E et al (2016) Orthostatic tremor: a cerebellar pathology? Brain 139:2182–2197

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hageman PA, Leibowitz JM, Blanke D (1995) Age and gender effects on postural control measures. Arch Phys Med Rehabil. 76:961–965

    Article  CAS  PubMed  Google Scholar 

  44. Gill J, Allum JHJ, Carpenter MG, Held-Ziolkowska M, Adkin AL, Honegger F et al (2001) Trunk sway measures of postural stability during clinical balance tests: effects of age. J Gerontol Ser A Biol Sci Med Sci. 56:438–447

    Article  Google Scholar 

  45. Feil K, Böttcher N, Guri F, Krafczyk S, Schöberl F, Zwergal A et al (2015) Long-term course of orthostatic tremor in serial posturographic measurement. Park Relat Disord. 21:905–910

    Article  CAS  Google Scholar 

  46. Fung VSC, Sauner D, Day BL (2001) A dissociation between subjective and objective unsteadiness in primary orthostatic tremor. Brain 124:322–330

    Article  CAS  PubMed  Google Scholar 

  47. Wuehr M, Schlick C, Möhwald K, Schniepp R (2018) Walking in orthostatic tremor modulates tremor features and is characterized by impaired gait stability. Sci Rep. 8:14152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kumar N, Kohlbecher S, Schneider E. A novel approach to video-based pupil tracking. Conf Proc—IEEE Int Conf Syst Man Cybern. 2009. pp 1255–62.

  49. Kirsch V, Keeser D, Hergenroeder T, Erat O, Ertl-Wagner B, Brandt T et al (2016) Structural and functional connectivity mapping of the vestibular circuitry from human brainstem to cortex. Brain Struct Funct. 221:1291–1308

    Article  CAS  PubMed  Google Scholar 

  50. Pradhan C, Wuehr M, Akrami F, Neuhaeusser M, Huth S, Brandt T et al (2015) Automated classification of neurological disorders of gait using spatio-temporal gait parameters. J Electromyogr Kinesiol. 25:413–422

    Article  PubMed  Google Scholar 

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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).

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Author notes

  1. Seyed-Ahmad Ahmadi and Gerome Vivar contributed equally to this work.

Authors and Affiliations

  1. German Center for Vertigo and Balance Disorders, Ludwig Maximilians Universität, Marchioninistr. 15, 81377, Munich, Germany

    Seyed-Ahmad Ahmadi, Gerome Vivar, Johann Frei, Stanislav Bardins, Thomas Brandt & Siegbert Krafczyk

  2. Computer Aided Medical Procedures, Technical University of Munich, 85748, Garching, Germany

    Seyed-Ahmad Ahmadi, Gerome Vivar & Johann Frei

  3. IMP Research Institute of Molecular Pathology, Campus-Vienna-Biocenter 1, 1030, Vienna, Austria

    Sergej Nowoshilow

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  1. Seyed-Ahmad Ahmadi
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Correspondence to Seyed-Ahmad Ahmadi.

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This manuscript is part of a supplement sponsored by the German Federal Ministry of Education and Research within the funding initiative for integrated research and treatment centers.

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Ahmadi, SA., Vivar, G., Frei, J. et al. Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway. J Neurol 266 (Suppl 1), 108–117 (2019). https://doi.org/10.1007/s00415-019-09458-y

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  • DOI: https://doi.org/10.1007/s00415-019-09458-y

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