Skip to main content

Advertisement

SpringerLink
Log in

Are smartphones and machine learning enough to diagnose tremor?

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

Abstract

Background

Patients with essential tremor (ET), Parkinson’s disease (PD) and dystonic tremor (DT) can be difficult to classify and often share similar characteristics.

Objectives

To use ubiquitous smartphone accelerometers with and without clinical features to automate tremor classification using supervised machine learning, and to use unsupervised learning to evaluate if natural clusterings of patients correspond to assigned clinical diagnoses.

Methods

A supervised machine learning classifier was trained to classify 78 tremor patients using leave-one-out cross-validation to estimate performance on unseen accelerometer data. An independent cohort of 27 patients were also studied. Next, we focused on a subset of 48 patients with both smartphone-based tremor measurements and detailed clinical assessment metrics and compared two separate machine learning classifiers trained on these data.

Results

The classifier yielded a total accuracy of 74.4% and F1-score of 0.74 for a trinary classification with an area under the curve of 0.904, average F1-score of 0.94, specificity of 97% and sensitivity of 84% in classifying PD from ET or DT. The algorithm classified ET from non-ET with 88% accuracy, but only classified DT from non-DT with 29% accuracy. A poorer performance was found in the independent cohort. Classifiers trained on accelerometer and clinical data respectively obtained similar results.

Conclusions

Machine learning classifiers achieved a high accuracy of PD, however moderate accuracy of ET, and poor accuracy of DT classification. This underscores the difficulty of using AI to classify some tremors due to lack of specificity in clinical and neuropathological features, reinforcing that they may represent overlapping syndromes.

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

Similar content being viewed by others

References

  1. Martino D, Espay AJ, Fasano A, Morgante F (2016) Rhythmical involuntary movements (tremor and tremor-like conditions). In: Martino D, Espay AJ, Fasano A, Morgante F (eds) Disorders of movement—a guide to diagnosis and treatment. Springer, Berlin, pp 207–263

    Google Scholar 

  2. Ure RJ, Dhanju S, Lang AE, Fasano A (2016) Unusual tremor syndromes: know in order to recognise. J Neurol Neurosurg Psychiatry 87(11):1191–1203. https://doi.org/10.1136/jnnp-2015-311693 (Epub 2016 Mar 16)

    Article  PubMed  Google Scholar 

  3. Rizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G (2016) Accuracy of clinical diagnosis of Parkinson disease A systematic review and meta-analysis. Neurology 86:566–576

    Article  Google Scholar 

  4. Bhatia KP, Bain P, Bajaj N et al (2018) Consensus Statement on the classification of tremors. from the task force on tremor of the International Parkinson and Movement Disorder Society: IPMDS Task Force on Tremor Consensus Statement. Mov Disord 33(1):75–87. https://doi.org/10.1002/mds.27121

    Article  PubMed  Google Scholar 

  5. Fasano A, Lang AE, Espay AJ (2018) What is “Essential” about essential tremor? A diagnostic placeholder. Mov Disord 33(1):58–61. https://doi.org/10.1002/mds.27288

    Article  PubMed  Google Scholar 

  6. Rajalingam R, Breen DP, Lang AE, Fasano A (2018) Essential tremor plus is more common than essential tremor: Insights from the reclassification of a cohort of patients with lower limb tremor. Parkinsonism Relat Disord 56:109–110. https://doi.org/10.1016/j.parkreldis.2018.06.029

    Article  PubMed  Google Scholar 

  7. Fearon C, Espay AJ, Lang AE et al (2019) Soft signs in movement disorders: friends or foes? J Neurol Neurosurg Psychiatry 90(8):961–962. https://doi.org/10.1136/jnnp-2018-318455

    Article  PubMed  Google Scholar 

  8. Louis ED (2016) Diagnosis and management of tremor. Continuum (Minneap Minn) 22:1143–1158

    Google Scholar 

  9. Bove F, Di Lazzaro G, Mulas D, Cocciolillo F, Di Giuda D, Bentivoglio AR (2018) A role for accelerometry in the differential diagnosis of tremor syndromes. Funct Neurol 33(1):45–49

    Article  CAS  Google Scholar 

  10. Mourcou Q, Fleury A, Franco C, Klopcic F, Vuillerme N (2015) Performance evaluation of smartphone inertial sensors measurement for range of motion. Sensors 15(9):23168–23187. https://doi.org/10.3390/s150923168

    Article  PubMed  PubMed Central  Google Scholar 

  11. Balachandar A, Fasano A (2017) Characterizing orthostatic tremor using a smartphone application. Tremor Other Hyperkinet Mov 7:488. https://doi.org/10.7916/D8V12GRJ.eCollection2017

    Article  Google Scholar 

  12. Bhatti D, Thompson R, Hellman A, Penke C, Bertoni JM, Torres-Russotto D (2017) smartphone apps provide a simple, accurate bedside screening tool for orthostatic tremor. Move Disord Clin Pract 4(6):852–857. https://doi.org/10.1002/mdc3.12547

    Article  Google Scholar 

  13. López-Blanco R, Velasco MA, Méndez-Guerrero A et al (2019) Smartwatch for the analysis of rest tremor in patients with Parkinsons disease. J Neurol Sci 401:37–42. https://doi.org/10.1016/j.jns.2019.04.011

    Article  PubMed  Google Scholar 

  14. di Biase L, Brittain JS, Shah SA et al (2017) Tremor stability index: a new tool for differential diagnosis in tremor syndromes. Brain 140:1977–1986

    Article  Google Scholar 

  15. Daneault JF, Carignan B, Codere CE, Sadikot AF, Duval C (2013) Using a smart phone as a standalone platform for detection and monitoring of pathological tremors. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2012.00357

    Article  PubMed  PubMed Central  Google Scholar 

  16. Barrantes S, Sánchez Egea AJ, González Rojas HA et al (2017) Differential diagnosis between Parkinson’s disease and essential tremor using the smartphone’s accelerometer. PLoS One 12:e0183843

    Article  Google Scholar 

  17. Jones E, Oliphant E, Peterson P (2001) SciPy: Open source scientific tools for python. http://www.scipy.org/

  18. Oliphant TE (2007) Python for scientific computing. Comput Sci Eng 9(3):10–20. https://doi.org/10.1109/mcse.2007.58

    Article  CAS  Google Scholar 

  19. Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. JMLR 12:2825–2830

    Google Scholar 

  20. Fahn S, Tolosa E, Conceppcion M (1993) Clinical rating scale for tremor. In: Jankovic J, Tolosa E (eds) Parkinson’s disease and movement disorders. Williams and Wilkins, Baltimore, pp 271–280

    Google Scholar 

  21. Goetz CG, Tilley BC, Shaftman SR et al (2008) Movement Disorder Society-sponsored revision of the Unified Parkinsons Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23(15):2129–2170. https://doi.org/10.1002/mds.22340

    Article  PubMed  Google Scholar 

  22. Burke RE, Fahn S, Marsden CD, Bressman SB, Moskowitz C, Friedman J (1985) Validity and reliability of a rating scale for the primary torsion dystonias. Neurology 35:73–77

    Article  CAS  Google Scholar 

  23. Schmitz-Hübsch T, du Montcel TS, Baliko L et al (2006) Scale for the assessment and rating of ataxia. Neurology 66:1717–1720. https://doi.org/10.1212/01.wnl.0000219042.60538.92

    Article  PubMed  Google Scholar 

  24. Espay AJ, Li JY, Johnston L, Chen R, Lang AE (2005) Mirror movements in parkinsonism: evaluation of a new clinical sign. J Neurol Neurosurg Psychiatry 76:1355–1358

    Article  CAS  Google Scholar 

  25. Abdo WF, Borm GF, Munneke M, Verbeek MM, Esselink RAJ, Bloem BR (2006) Ten steps to identify atypical parkinsonism. J Neurol Neurosurg Psychiatry 77(12):1367–1369. https://doi.org/10.1136/jnnp.2006.091322

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sternberg EJ, Alcalay RN, Levy OA, Louis ED. The “head snap”: a subtle clinical feature during the finger-nose-finger maneuver in essential tremor. Tremor Other Hyperkinet Mov (N Y). 2013; 3: pii: tre-03–159–3719–1

  27. Kulkarni O, Lafaver K, Tarsy D (2013) The, “floating door sign” in Parkinson’s disease. Parkinsonism Relat Disord 19:825–826

    Article  CAS  Google Scholar 

  28. Lieberman A, Deep A, Shi J et al (2017) Downward finger displacement distinguishes Parkinson disease dementia from Alzheimer disease. Int J Neurosci 128(2):151–154. https://doi.org/10.1080/00207454.2017.1379518

    Article  PubMed  Google Scholar 

  29. Bajaj NP, Knöbel M, Gontu V, Bain PG (2011) Can spiral analysis predict the FP-CIT SPECT scan result in tremulous patients? Mov Disord 26(4):699–704. https://doi.org/10.1002/mds.23507

    Article  PubMed  Google Scholar 

  30. Krismer F, Pinter B, Mueller C et al (2017) Sniffing the diagnosis: olfactory testing in neurodegenerative parkinsonism. Parkinsonism Relat Disord 35:36–41. https://doi.org/10.1016/j.parkreldis.2016.11.010

    Article  CAS  PubMed  Google Scholar 

  31. Stiasny-Kolster K, Mayer G, Schafer S, Moller JC, Heinzel-Gutenbrunner M, Oertel WH (2007) The REM sleep behavior disorder screening questionnaire—a new diagnostic instrument. Mov Disord 22:2386–2393

    Article  Google Scholar 

  32. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J (1961) An inventory for measuring depression. Arch Gen Psychiatry 4:561–571

    Article  CAS  Google Scholar 

  33. Nasreddine ZS, Phillips NA, Bédirian V et al (2005) The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53(4):695–699. https://doi.org/10.1111/j.1532-5415.2005.53221.x

    Article  PubMed  Google Scholar 

  34. Visser M, Marinus J, Stiggelbout AM, Hilten JJV (2004) Assessment of autonomic dysfunction in Parkinsons disease: the SCOPA-AUT. Mov Disord 19(11):1306–1312. https://doi.org/10.1002/mds.20153

    Article  PubMed  Google Scholar 

  35. McInnes L, Healy J (2018) UMAP: uniform manifold approximation and projection for dimension reduction. ArXiv e-prints. https://arxiv.org/1802.0342

  36. Kearns M, Ron D (1999) Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Comput 11:1427–1453

    Article  CAS  Google Scholar 

  37. Powers DM (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol 2(1):37–63

    Google Scholar 

  38. Lipton ZC, Elkan C, Naryanaswamy B (2014) Optimal thresholding of classifiers to maximize F1 measure. In: Machine learning and knowledge discovery in databases lecture notes in computer science. pp 225–239. doi: https://doi.org/10.1007/978-3-662-44851-9_15

  39. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manage 45:427–437

    Article  Google Scholar 

  40. Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE. https://doi.org/10.1371/journal.pone.0118432

    Article  PubMed  PubMed Central  Google Scholar 

  41. Cohen JP, Cao T, Viviano JD, Huang CW et al (2021) Problems in the deployment of machine-learned models in health care. CMAJ 193(35):E1391–E1394. https://doi.org/10.1503/cmaj.202066 (Epub 2021 Aug 30)

    Article  PubMed  PubMed Central  Google Scholar 

  42. Knoebel M, Bain PG (2009) Spirography in essential tremor and parkinsonism. Mov Disord 249(24 Suppl. 1):S503

    Google Scholar 

  43. Shah M, Muhammed N, Findley LJ, Hawkes CH (2008) Olfactory tests in the diagnosis of essential tremor. Parkinsonism Relat Disord 14:563–568

    Article  Google Scholar 

  44. Schneider SA, Edwards MJ, Mir P et al (2007) Patients with adult-onset dystonic tremor resembling parkinsonian tremor have scans without evidence of dopaminergic deficit (SWEDDs). Mov Disord 22:2210–2215

    Article  Google Scholar 

  45. Elble RJ (2013) Defining dystonic tremor. Curr Neuropharmacol 11:48–52

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Figueroa RL, Zeng-Treitler Q, Kandula S, Ngo LH (2012) Predicting sample size required for classification performance. BMC Med Inform Decis Making. https://doi.org/10.1186/1472-6947-12-8

    Article  Google Scholar 

  47. Sun C, Shrivastava A, Singh S, Gupta A (2017) Revisiting unreasonable effectiveness of data in deep learning era. ArXiv e-prints. http://arxiv.org/1707.02968

  48. He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2008.239

    Article  Google Scholar 

  49. Branco P, Torgo L, Ribeiro RP (2016) A survey of predictive modeling on imbalanced domains. ACM Comput Surv 49(2):50. https://doi.org/10.1145/2907070

    Article  Google Scholar 

  50. Fasano A, Mancini M (2020) How Complex is Reality? Wearable-based mobility monitoring: the long road ahead. Lancet Neurol. https://doi.org/10.1016/S1474-4422(20)30033-8 (Epub ahead of print)

    Article  PubMed  Google Scholar 

  51. Elble RJ, McNames J (2016) Using portable transducers to measure tremor severity. Tremor Other Hyperkinet Mov (N Y) 6:375

    Article  Google Scholar 

  52. López-Blanco R, Velasco MA, Méndez-Guerrero A et al (2018) Essential tremor quantification based on the combined use of a smartphone and a smartwatch: the NetMD study. J Neurosci Methods 303:95–102. https://doi.org/10.1016/j.jneumeth.2018.02.015

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Authors are grateful to Dr. Hamza Jalal for the help in setting up the experimental protocol.

Funding

AB, MA, LM, AS, WDH, AB—Stock Ownership in medically-related fields: none, Intellectual Property Rights: none, Consultancies: none, Expert Testimony: none, Advisory Boards: none, Employment: none, Partnerships: none, Contracts: none, Honoraria: none, Royalties: none, Grants: none, Other: none. LO—Stock Ownership in medically-related fields: none, Intellectual Property Rights: none, Consultancies: none, Expert Testimony: none, Advisory Boards: none, Employment: none, Partnerships: none, Contracts: none, Honoraria: none, Royalties: none, Grants: none, Other: Funding for travel from Medtronic. AF—Stock Ownership in medically-related fields: none, Intellectual Property Rights: none, Consultancies: Abbvie, Medtronic, Boston Scientific, Sunovion, Chiesi farmaceutici, UCB, Ipsen, Expert Testimony: none, Advisory Boards: Abbvie, Boston Scientific, Ipsen, Employment: none, Partnerships: none, Contracts: none, Honoraria: Abbvie, Medtronic, Boston Scientific, Sunovion, Chiesi farmaceutici, UCB, Ipsen, Royalties: none, Grants: University of Toronto, Weston foundation, Abbvie, Medtronic, Boston Scientific, Other: none.

Author information

Authors and Affiliations

  1. Department of Medicine, University of Toronto, Toronto, Canada

    Arjun Balachandar

  2. Division of Neurology, Edmond J. Safra Program in Parkinson’s Disease and Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, University of Toronto, 399 Bathurst St, 7MacL412, Toronto, ON, M5T 2S8, Canada

    Musleh Algarni, Lais Oliveira & Alfonso Fasano

  3. James J and Joan A. Gardner Center for Parkinson’s Disease and Movement Disorders, University of Cincinnati Medical Center, Cincinnati, OH, USA

    Luca Marsili, Andrea Sturchio & Alberto J. Espay

  4. Department of Neurology, Ohio State University Wexner Medical Center, Columbus, OH, USA

    Aristide Merola

  5. Department of Physiology, University of Toronto, Toronto, ON, Canada

    William D. Hutchison

  6. Krembil Research Institute, Toronto, ON, Canada

    William D. Hutchison & Alfonso Fasano

  7. Department of Mathematics, Indian Institute of Science, Bangalore, India

    Aniruddh Balasubramaniam

  8. Department of Computer Science, University of Toronto, Toronto, ON, Canada

    Frank Rudzicz

  9. Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, ON, Canada

    Frank Rudzicz

  10. Vector Institute for Artificial Intelligence, Toronto, ON, Canada

    Frank Rudzicz

  11. CenteR for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada

    Alfonso Fasano

Authors
  1. Arjun Balachandar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Musleh Algarni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lais Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luca Marsili
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aristide Merola
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrea Sturchio
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alberto J. Espay
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. William D. Hutchison
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Aniruddh Balasubramaniam
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Frank Rudzicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alfonso Fasano
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AB—Research project: execution; Statistical analysis: design, execution; Manuscript: writing of the first draft. MA—Research project: execution; Manuscript: review and critique. LO—Research project: execution; Manuscript: review and critique. LM—Research project: execution; Manuscript: review and critique. AM— Research project: execution; Manuscript: review and critique. AS—Research project: organization, execution; Statistical analysis: review and critique; Manuscript: review and critique. AJE—Research project: organization, execution; Statistical analysis: review and critique; Manuscript: review and critique. WDH—Statistical analysis: review and critique; Manuscript: review and critique. AB—Statistical analysis: execution, review and critique; Manuscript: review and critique. FR—Research project: conception; Statistical analysis: design, review and critique; Manuscript: review and critique. AF—Research project: conception, organization; Statistical analysis: review and critique; Manuscript: review and critique.

Corresponding author

Correspondence to Alfonso Fasano.

Ethics declarations

Conflicts of interest

AJE is chair of the Task Force on Technology of the IPMDS. He has received research support from Great Lakes Neurotechnology. AF sits in the advisory board of Evotion and received honoraria from Apple for an unrelated project.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 39 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balachandar, A., Algarni, M., Oliveira, L. et al. Are smartphones and machine learning enough to diagnose tremor?. J Neurol 269, 6104–6115 (2022). https://doi.org/10.1007/s00415-022-11293-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00415-022-11293-7

Keywords

Search

Navigation