Skip to main content

Advertisement

SpringerLink
Log in

Machine learning models help differentiate between causes of recurrent spontaneous vertigo

  • Original Communication
  • Published:
Journal of Neurology Aims and scope Submit manuscript

Abstract

Background

Vestibular migraine (VM) and Menière’s disease (MD) are two common causes of recurrent spontaneous vertigo. Using history, video-nystagmography and audiovestibular tests, we developed machine learning models to separate these two disorders.

Methods

We recruited patients with VM or MD from a neurology outpatient facility. One hundred features from six “feature subsets”: history, acute video-nystagmography and four laboratory tests (video head impulse test, vestibular-evoked myogenic potentials, caloric testing and audiogram) were used. We applied ten machine learning algorithms to develop classification models. Modelling was performed using three “tiers” of data availability to simulate three clinical settings. “Tier 1” used all available data to simulate the neuro-otology clinic, “Tier 2” used only history, audiogram and caloric test data, representing the general neurology clinic, and “Tier 3” used history alone as occurs in primary care. Model performance was evaluated using tenfold cross-validation.

Results

Data from 160 patients with VM and 114 with MD were used for model development. All models effectively separated the two disorders for all three tiers, with accuracies of 85.77–97.81%. The best performing algorithms (AdaBoost and Random Forest) yielded accuracies of 97.81% (95% CI 95.24–99.60), 94.53% (91.09–99.52%) and 92.34% (92.28–96.76%) for tiers 1, 2 and 3. The best feature subset combination was history, acute video-nystagmography, video head impulse test and caloric testing, and the best single feature subset was history.

Conclusions

Machine learning models can accurately differentiate between VM and MD and are promising tools to assist diagnosis by medical practitioners with diverse levels of expertise and resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The dataset used for this study contains patient health data and is not publicly available for privacy reasons. A deidentified version of dataset is available on reasonable request from the corresponding author M.W. The data will be shared through a data sharing agreement. With this mediated access, the data are FAIR compliant.

References

  1. Australian Bureau of Statistics (2021) Population: Census. https://www.abs.gov.au/statistics/people/population/population-census/latest-release. Accessed 14 Aug 2022

  2. Baier B, Stieber N, Dieterich M (2009) Vestibular-evoked myogenic potentials in vestibular migraine. J Neurol 256:1447–1454. https://doi.org/10.1007/s00415-009-5132-4

    Article  PubMed  Google Scholar 

  3. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  4. Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, San Francisco, California, USA, pp 785–794

  5. Colebatch JG, Halmagyi GM, Skuse NF (1994) Myogenic potentials generated by a click-evoked vestibulocollic reflex. J Neurol Neurosurg Psychiatry 57:190–197. https://doi.org/10.1136/jnnp.57.2.190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  7. Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551–585

    Google Scholar 

  8. Dieterich M, Obermann M, Celebisoy N (2016) Vestibular migraine: the most frequent entity of episodic vertigo. J Neurol 263:82–89. https://doi.org/10.1007/s00415-015-7905-2

    Article  CAS  PubMed Central  Google Scholar 

  9. Exarchos TP, Rigas G, Bibas A, Kikidis D, Nikitas C, Wuyts FL, Ihtijarevic B, Maes L, Cenciarini M, Maurer C, Macdonald N, Bamiou DE, Luxon L, Prasinos M, Spanoudakis G, Koutsouris DD, Fotiadis DI (2016) Mining balance disorders’ data for the development of diagnostic decision support systems. Comput Biol Med 77:240–248. https://doi.org/10.1016/j.compbiomed.2016.08.016

    Article  CAS  PubMed  Google Scholar 

  10. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(1189–1232):1144

    Google Scholar 

  11. Groezinger M, Huppert D, Strobl R, Grill E (2020) Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry. J Neurol 267:160–167. https://doi.org/10.1007/s00415-020-10061-9

    Article  PubMed  PubMed Central  Google Scholar 

  12. Gürkov R, Jerin C, Flatz W, Maxwell R (2019) Clinical manifestations of hydropic ear disease (Menière’s). Eur Arch Otorhinolaryngol 276:27–40. https://doi.org/10.1007/s00405-018-5157-3

    Article  PubMed  Google Scholar 

  13. Huang CH, Wang SJ, Young YH (2011) Localization and prevalence of hydrops formation in Ménière’s disease using a test battery. Audiol Neurootol 16:41–48. https://doi.org/10.1159/000312199

    Article  PubMed  Google Scholar 

  14. Inoue A, Egami N, Fujimoto C, Kinoshita M, Yamasoba T, Iwasaki S (2016) Vestibular evoked myogenic potentials in vestibular migraine: do they help differentiating from Menière’s disease? Ann Otol Rhinol Laryngol 125:931–937. https://doi.org/10.1177/0003489416665192

    Article  PubMed  Google Scholar 

  15. Jongkees LB, Maas JP, Philipszoon AJ (1962) Clinical nystagmography. A detailed study of electro-nystagmography in 341 patients with vertigo. Pract Otorhinolaryngol (Basel) 24:65–93

    CAS  PubMed  Google Scholar 

  16. Kabade V, Hooda R, Raj C, Awan Z, Young AS, Welgampola MS, Prasad M (2021) Machine learning techniques for differential diagnosis of vertigo and dizziness: a review. Sensors 21:7565

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lempert T, Olesen J, Furman J, Waterston J, Seemungal B, Carey J, Bisdorff A, Versino M, Evers S, Newman-Toker D (2012) Vestibular migraine: diagnostic criteria. J Vestib Res 22:167–172. https://doi.org/10.3233/VES-2012-0453

    Article  PubMed  Google Scholar 

  18. Lopez-Escamez JA, Carey J, Chung W-H, Goebel JA, Magnusson M, Mandalà M, Newman-Toker DE, Strupp M, Suzuki M, Trabalzini F, Bisdorff A (2015) Diagnostic criteria for Menière’s disease. J Vestib Res 25:1–7. https://doi.org/10.3233/VES-150549

    Article  PubMed  Google Scholar 

  19. Lopez-Escamez JA, Dlugaiczyk J, Jacobs J, Lempert T, Teggi R, von Brevern M, Bisdorff A (2014) Accompanying symptoms overlap during attacks in Menière’s disease and vestibular migraine. Front Neurol. https://doi.org/10.3389/fneur.2014.00265

    Article  PubMed  PubMed Central  Google Scholar 

  20. MacDougall HG, McGarvie LA, Halmagyi GM, Curthoys IS, Weber KP (2013) The video head impulse test (vHIT) detects vertical semicircular canal dysfunction. PLoS ONE 8:e61488. https://doi.org/10.1371/journal.pone.0061488

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. MacDougall HG, Weber KP, McGarvie LA, Halmagyi GM, Curthoys IS (2009) The video head impulse test: diagnostic accuracy in peripheral vestibulopathy. Neurology 73:1134–1141. https://doi.org/10.1212/WNL.0b013e3181bacf85

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Medical Board of Australia (2021) Medical Board of Australia Registrant Data—reporting Period: 01 October 2021 to 31 December 2021. Melbourne, Australia. https://www.ahpra.gov.au/documents/default.aspx?record=WD22%2f31646&dbid=AP&chksum=ccbe7hvigrjZ4H64z42Aaw%3d%3d. Accessed 14 Aug 2022

  23. Muelleman T, Shew M, Subbarayan R, Shum A, Sykes K, Staecker H, Lin J (2017) Epidemiology of Dizzy patient population in a neurotology clinic and predictors of peripheral etiology. Otol Neurotol 38:870–875. https://doi.org/10.1097/mao.0000000000001429

    Article  PubMed  Google Scholar 

  24. Neff BA, Staab JP, Eggers SD, Carlson ML, Schmitt WR, Van Abel KM, Worthington DK, Beatty CW, Driscoll CL, Shepard NT (2012) Auditory and vestibular symptoms and chronic subjective dizziness in patients with Ménière’s disease, vestibular migraine, and Ménière’s disease with concomitant vestibular migraine. Otol Neurotol 33:1235–1244. https://doi.org/10.1097/MAO.0b013e31825d644a

    Article  PubMed  Google Scholar 

  25. Neuhauser HK, von Brevern M, Radtke A, Lezius F, Feldmann M, Ziese T, Lempert T (2005) Epidemiology of vestibular vertigo. A neurotologic survey of the general population 65:898–904. https://doi.org/10.1212/01.wnl.0000175987.59991.3d

    Article  CAS  Google Scholar 

  26. Nham B, Reid N, Bein K, Bradshaw A, McGarvie L, Argaet E, Young A, Watson S, Halmagyi G, Black D, Welgampola M (2021) Capturing vertigo in the emergency room: three tools to double the rate of diagnosis. J Neurol. https://doi.org/10.1007/s00415-021-10627-1

    Article  PubMed  Google Scholar 

  27. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  28. Peterson LE (2009) K-nearest neighbor. Scholarpedia 4:1883. https://doi.org/10.4249/scholarpedia.188

    Article  Google Scholar 

  29. Polensek SH, Tusa RJ (2010) Nystagmus during attacks of vestibular migraine: an aid in diagnosis. Audiol Neurotol 15:241–246. https://doi.org/10.1159/000255440

    Article  Google Scholar 

  30. Quinlan JR (1996) Learning decision tree classifiers. ACM Comput Surv 28:71–72. https://doi.org/10.1145/234313.234346

    Article  Google Scholar 

  31. Radtke A, Lempert T, Gresty MA, Brookes GB, Bronstein AM, Neuhauser H (2002) Migraine and Ménière’s disease: is there a link? Neurology 59:1700–1704. https://doi.org/10.1212/01.wnl.0000036903.22461.39

    Article  CAS  PubMed  Google Scholar 

  32. Radtke A, Neuhauser H, von Brevern M, Hottenrott T, Lempert T (2011) Vestibular migraine–validity of clinical diagnostic criteria. Cephalalgia 31:906–913. https://doi.org/10.1177/0333102411405228

    Article  PubMed  Google Scholar 

  33. Rauch SD, Zhou G, Kujawa SG, Guinan JJ, Herrmann BS (2004) Vestibular evoked myogenic potentials show altered tuning in patients with Ménière’s disease. Otol Neurotol 25:333–338. https://doi.org/10.1097/00129492-200405000-00022

    Article  PubMed  Google Scholar 

  34. Rosenblatt F (1961) Principles of neurodynamics. perceptrons and the theory of brain mechanisms. In:Cornell Aeronautical Lab Inc Buffalo NY

  35. Rosengren SM, McAngus Todd NP, Colebatch JG (2005) Vestibular-evoked extraocular potentials produced by stimulation with bone-conducted sound. Clin Neurophysiol 116:1938–1948. https://doi.org/10.1016/j.clinph.2005.03.019

    Article  CAS  PubMed  Google Scholar 

  36. Schapire RE (2013) Explaining adaboost. Empirical inference. Springer, Berlin, pp 37–52

    Chapter  Google Scholar 

  37. Taylor RL, Zagami AS, Gibson WPR, Black DA, Watson SRD, Halmagyi MG, Welgampola MS (2012) Vestibular evoked myogenic potentials to sound and vibration: characteristics in vestibular migraine that enable separation from Menière’s disease. Cephalalgia 32:213–225. https://doi.org/10.1177/0333102411434166

    Article  PubMed  Google Scholar 

  38. Vivar G, Strobl R, Grill E, Navab N, Zwergal A, Ahmadi SA (2021) Using base-ml to learn classification of common vestibular disorders on DizzyReg Registry data. Front Neurol 12:681140. https://doi.org/10.3389/fneur.2021.681140

    Article  PubMed  PubMed Central  Google Scholar 

  39. von Brevern M, Zeise D, Neuhauser H, Clarke AH, Lempert T (2004) Acute migrainous vertigo: clinical and oculographic findings. Brain 128:365–374. https://doi.org/10.1093/brain/awh351

    Article  Google Scholar 

  40. Wright RE (1995) Logistic regression. Reading and understanding multivariate statistics. American Psychological Association, Washington, DC, pp 217–244

    Google Scholar 

  41. Young AS, Lechner C, Bradshaw AP, MacDougall HG, Black DA, Halmagyi GM, Welgampola MS (2019) Capturing acute vertigo. A vestibular event monitor 92:e2743–e2753. https://doi.org/10.1212/wnl.0000000000007644

    Article  Google Scholar 

  42. Young AS, Nham B, Bradshaw AP, Calic Z, Pogson JM, D’Souza M, Halmagyi GM, Welgampola MS (2021) Clinical, oculographic, and vestibular test characteristics of vestibular migraine. Cephalalgia 41:1039–1052. https://doi.org/10.1177/03331024211006042

    Article  PubMed  Google Scholar 

  43. Young AS, Nham B, Bradshaw AP, Calic Z, Pogson JM, Gibson WP, Halmagyi GM, Welgampola MS (2021) Clinical, oculographic and vestibular test characteristics of Ménière’s disease. J Neurol. https://doi.org/10.1007/s00415-021-10699-z

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zhang Y, Kong Q, Chen J, Li L, Wang D, Zhou J (2016) International Classification of Headache Disorders 3rd edition beta-based field testing of vestibular migraine in China: Demographic, clinical characteristics, audiometric findings and diagnosis statues. Cephalalgia 36:240–248. https://doi.org/10.1177/0333102415587704

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported by funding from the Garnett Passe and Rodney Williams Memorial Foundation (Grant 2021_RS_Wang).

Author information

Authors and Affiliations

  1. Institute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, Australia

    Chao Wang, Andrew P. Bradshaw, Benjamin Nham, Sally M. Rosengren, David Burke, G. Michael Halmagyi & Miriam S. Welgampola

  2. Central Clinical School, University of Sydney, Sydney, Australia

    Chao Wang, Allison S. Young, Benjamin Nham, Sally M. Rosengren, David Burke, G. Michael Halmagyi & Miriam S. Welgampola

  3. School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Chahat Raj, Gnana K. Bharathy & Mukesh Prasad

  4. Department of Neurophysiology, Liverpool Hospital, Sydney, Australia

    Zeljka Calic

  5. South Western Sydney Clinical School, University of New South Wales, Sydney, Australia

    Zeljka Calic

Authors
  1. Chao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Allison S. Young
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chahat Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew P. Bradshaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Benjamin Nham
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sally M. Rosengren
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zeljka Calic
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. David Burke
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. G. Michael Halmagyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Gnana K. Bharathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Mukesh Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Miriam S. Welgampola
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miriam S. Welgampola.

Ethics declarations

Conflicts of interest

The authors declare that they have no competing interests.

Ethics approval

This study was approved by the Sydney Local Health District Ethics Committee (Protocol No X21-0295) and performed in accordance with the 1964 Declaration of Helsinki and its later amendments.

Informed consent

Written informed consent was obtained from all participants.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Young, A.S., Raj, C. et al. Machine learning models help differentiate between causes of recurrent spontaneous vertigo. J Neurol (2024). https://doi.org/10.1007/s00415-023-11997-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00415-023-11997-4

Keywords

Search

Navigation