A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders

Julian Varghese, Stephan Niewöhner, Iñaki Soto-Rey, Stephanie Schipmann-Miletić, Nils Warneke, Tobias Warnecke, Martin Dugas, Julian Varghese, Stephan Niewöhner, Iñaki Soto-Rey, Stephanie Schipmann-Miletić, Nils Warneke, Tobias Warnecke, Martin Dugas

Abstract

Parkinson's disease and Essential Tremor are two of the most common movement disorders and are still associated with high rates of misdiagnosis. Collected data by technology-based objective measures (TOMs) has the potential to provide new promising and highly accurate movement data for a better understanding of phenotypical characteristics and diagnostic support. A technology-based system called Smart Device System (SDS) is going to be implemented for multi-modal high-resolution acceleration measurement of patients with PD or ET within a clinical setting. The 2-year prospective observational study is conducted to identify new phenotypical biomarkers and train an Artificial Intelligence System. The SDS is going to be integrated and tested within a 20-min assessment including smartphone-based questionnaires, two smartwatches at both wrists and tablet-based Archimedean spirals drawing for deeper tremor-analyses. The electronic questionnaires will cover data on medication, family history and non-motor symptoms. In this paper, we describe the steps for this novel technology-utilizing examination, the principal steps for data analyses and the targeted performances of the system. Future work considers integration with Deep Brain Stimulation, dissemination into further sites and patient's home setting as well as integration with further data sources as neuroimaging and biobanks. Study Registration ID on ClinicalTrials.gov: NCT03638479.

Keywords: Essential Tremor; Parkinson's Disease; artificial intelligence; neural networks; smart wearables.

Figures

Figure 1
Figure 1
Overview of the Smart Device System.
Figure 2
Figure 2
Archimedean spiral to be drawn with a pressure-sensing stylus on a 10.5 inch screen on an Apple iPad Touch, starting from the point in the middle.
Figure 3
Figure 3
Overview screen (left): the full examination takes approximately 20-min and starts with questionnaires and heart frequency measurement by the smartwatch. Before hand-tremor assessment starts, smart-watch calibration is required. Questionnaire screen (upper right): showing one item of the PD-NMS instrument. Initial signal processing on the smartphone (bottom right): visualization of average amplitude and frequency, raw acceleration data during examination section 3 and frequency spectrum. The acceleration data illustrates a simulated case of re-emergent tremor on the time axis. FFT, Fast Fourier Transform. Data capture and analyses of tablet-based spiral drawing is not implemented yet.

References

    1. Louis ED, Ottman R, Hauser WA. How common is the most common adult movement disorder? Estimates of the prevalence of essential tremor throughout the world. Mov Disord. (1998) 13:5–10. 10.1002/mds.870130105
    1. Newman EJ, Breen K, Patterson J, Hadley DM, Grosset KA, Grosset DG. Accuracy of Parkinson's disease diagnosis in 610 general practice patients in the West of Scotland. Mov Disord. (2009) 24:2379–85. 10.1002/mds.22829
    1. Jain S, Lo SE, Louis ED. Common misdiagnosis of a common neurological disorder: how are we misdiagnosing essential tremor? Arch Neurol. (2006) 63:1100–4. 10.1001/archneur.63.8.1100
    1. Heldman DA, Espay AJ, LeWitt PA, Giuffrida JP. Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson's disease. Parkinsonism Relat Disord. (2014) 20:590–5. 10.1016/j.parkreldis.2014.02.022
    1. Papapetropoulos S, Mitsi G, Espay AJ. Digital health revolution: is it time for affordable remote monitoring for Parkinson's disease? Front Neurol. (2015) 6:34. 10.3389/fneur.2015.00034
    1. Silva de Lima AL, Hahn T, Evers LJW, Vries NM de, Cohen E, Afek M, et al. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease. PLoS ONE (2017) 12:e0189161 10.1371/journal.pone.0189161
    1. Espay AJ, Bonato P, Nahab FB, Maetzler W, Dean JM, Klucken J, et al. . Technology in Parkinson's disease: challenges and opportunities. Mov Disord. (2016) 31:1272–82. 10.1002/mds.26642
    1. Zham P, Arjunan S, Raghav S, Kumar DK. Efficacy of guided spiral drawing in the classification of Parkinson's disease. IEEE J Biomed Health Inform. (2017) 22:1648–52. 10.1109/JBHI.2017.2762008
    1. Zhang B, Huang F, Liu J, Zhang D. A novel posture for better differentiation between Parkinson's tremor and essential tremor. Front Neurosci. (2018) 12:317. 10.3389/fnins.2018.00317
    1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature (2015) 521:436–44. 10.1038/nature14539
    1. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. . Dermatologist-level classification of skin cancer with deep neural networks. Nature (2017) 542:115–8. 10.1038/nature21056
    1. Dugas M, Neuhaus P, Meidt A, Doods J, Storck M, Bruland P, et al. . Portal of medical data models: Information infrastructure for medical research and healthcare. Database (Oxford) (2016) 2016:bav121. 10.1093/database/bav121
    1. Deuschl G, Bhatia KP, Elble R, Hallett M. Understanding the new tremor classification. Mov Disord. (2018) 33:1267–8. 10.1002/mds.27368
    1. Gövert F, Becktepe J, Deuschl G. Die neue Tremorklassifikation der International Parkinson and Movement Disorder Society. Der Nervenarzt (2018) 89:376–85. 10.1007/s00115-018-0489-1
    1. Hopfner F, Deuschl G. Is essential tremor a single entity? Eur J Neurol. (2018) 25:71–82. 10.1111/ene.13454
    1. Papengut F, Raethjen J, Binder A, Deuschl G. Rest tremor suppression may separate essential from parkinsonian rest tremor. Parkinsonism Relat Disord. (2013) 19:693–7. 10.1016/j.parkreldis.2013.03.013
    1. Alty JE, Kempster PA. A practical guide to the differential diagnosis of tremor. Postgrad Med J. (2011) 87:623–9. 10.1136/pgmj.2009.089623
    1. Bötzel K, Tronnier V, Gasser T. The differential diagnosis and treatment of tremor. Dtsch Arztebl Int. (2014) 111:225–35; quiz 236. 10.3238/arztebl.2014.0225
    1. Varghese J. Examination Video Supplement for Smartwatch-Based Data Capturing. (2018). Available online at: (Accessed October 5, 2018).
    1. Bangor A, Kortum PT, Miller JT. An empirical evaluation of the system usability scale. Int J Hum Comput Interact. (2008) 24:574–94. 10.1080/10447310802205776
    1. Natarajan P, Frenzel JC, Smaltz DH. Demystifying Big Data Machine Learning for Healthcare. Taylor & Francis (2017). Available online at:
    1. Food Drug Administration. November 2017 510(k) Clearances, Including Kardia Band System (2017). Available online at: (Accessed August 29, 2018).
    1. Paschen S, Deuschl G. Patient evaluation and selection for movement disorders surgery: the changing spectrum of indications. Prog Neurol Surg. (2018) 33:80–93. 10.1159/000480910
    1. Deuschl G, Paschen S, Witt K. Clinical outcome of deep brain stimulation for Parkinson's disease. Handb Clin Neurol. (2013) 116:107–28. 10.1016/B978-0-444-53497-2.00010-3
    1. Fasano A, Romito LM, Daniele A, Piano C, Zinno M, Bentivoglio AR, et al. . Motor and cognitive outcome in patients with Parkinson's disease 8 years after subthalamic implants. Brain (2010) 133:2664–76. 10.1093/brain/awq221
    1. Deuschl G, Raethjen J, Hellriegel H, Elble R. Treatment of patients with essential tremor. Lancet Neurol. (2011) 10:148–61. 10.1016/S1474-4422(10)70322-7

Source: PubMed

Sponsorer och medarbetare

Medicinska tillstånd

Läkemedelsinterventioner

3
Prenumerera