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Traditional Ordinal Strategies for Establishing the Severity and Status of Movement Disorders, Such as Parkinson’s Disease and Essential Tremor

Part of the Smart Sensors, Measurement and Instrumentation book series (SSMI,volume 31)

Abstract

Ordinal scale strategies are standardly applied to diagnose the severity of neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor. A clinician is tasked with the challenge of assigning an ordinal parameter based on a series of criteria to quantify a subjectively observed interpretation. Multiple ordinal scale systems exist for evaluating movement disorder symptoms. However, the issue is the uncertainty of translating the findings of one scale to another. The Unified Parkinson’s Disease Rating Scale (UPDRS) and upgraded Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) are commonly utilized for evaluating Parkinson’s disease severity. The Fahn-Tolosa-Marin Tremor Rating Scale is prevalently applied for Essential tremor. There are issues of concern regarding the application of ordinal scale approaches for determining the state of progressive neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor. The reliability of ordinal scale systems has not been conclusively established, and interpretive disparity is apparent respective of experience. A novel resolution is the introduction of wearable and wireless inertial sensor systems to objectively quantify movement disorder tremor. The inertial signal (accelerometer and/or gyroscope) can readily record the intrinsic characteristics of tremor for both Parkinson’s disease and Essential tremor. Successful testing and evaluation have even demonstrated the efficacy of deep brain stimulation systems for Parkinson’s disease and Essential tremor using a smartphone as a wearable and wireless inertial sensor system. These findings enable the pathways for developing Network Centric Therapy, which is in essence the emergence of the Internet of Things for healthcare regarding the domains of robustly diagnosing severity of neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor.

Keywords

  • Ordinal scale
  • Parkinson’s disease
  • Essential tremor
  • Unified Parkinson’s Disease Rating Scale (UPDRS)
  • Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)
  • Fahn-Tolosa-Marin Tremor Rating Scale
  • Wearable and wireless system
  • Network Centric Therapy

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  • DOI: 10.1007/978-981-13-5808-1_3
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References

  1. LeMoyne R, Coroian C, Cozza M, Opalinski P, Mastroianni T, Grundfest W (2009) The merits of artificial proprioception, with applications in biofeedback gait rehabilitation concepts and movement disorder characterization. In: Biomedical engineering. InTech, Vienna, pp 165–198

    Google Scholar 

  2. Ramaker C, Marinus J, Stiggelbout AM, Van Hilten BJ (2002) Systematic evaluation of rating scales for impairment and disability in Parkinson’s disease. Mov Disord 17(5):867–876

    CrossRef  Google Scholar 

  3. Post B, Merkus MP, de Bie RM, de Haan RJ, Speelman JD (2005) Unified Parkinson’s disease rating scale motor examination: are ratings of nurses, residents in neurology, and movement disorders specialists interchangeable? Mov Disord 20(12):1577–1584

    CrossRef  Google Scholar 

  4. Goetz CG, Stebbins GT, Chmura TA, Fahn S, Poewe W, Tanner CM (2010) Teaching program for the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale: (MDS-UPDRS). Mov Disord 25(9):1190–1194

    CrossRef  Google Scholar 

  5. Movement Disorder Society Task Force on Rating Scales for Parkinson’s Disease (2003) The Unified Parkinson’s Disease Rating Scale (UPDRS): status and recommendations. Mov Disord 18(7):738–750

    Google Scholar 

  6. López-Blanco R, Velasco MA, Méndez-Guerrero A, Romero JP, del Castillo MD, Serrano JI, Benito-León J, Bermejo-Pareja F, Rocon E (2018) Essential tremor quantification based on the combined use of a smartphone and a smartwatch: the NetMD study. J Neurosci Methods 303:95–102

    CrossRef  Google Scholar 

  7. Zheng X, Vieira Campos A, Ordieres-Meré J, Balseiro J, Labrador Marcos S, Aladro Y (2017) Continuous monitoring of essential tremor using a portable system based on smartwatch. Front Neurol 8(96):1–9

    Google Scholar 

  8. LeMoyne R, Coroian C, Mastroianni T (2009) Quantification of Parkinson’s disease characteristics using wireless accelerometers. In: ICME International conference on IEEE Complex Medical Engineering (CME), pp 1–5

    Google Scholar 

  9. LeMoyne R, Mastroianni T, Cozza M, Coroian C, Grundfest W (2010) Implementation of an iPhone for characterizing Parkinson’s disease tremor through a wireless accelerometer application. In: 32nd Annual international conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 4954–4958

    Google Scholar 

  10. LeMoyne R (2013) Wearable and wireless accelerometer systems for monitoring Parkinson’s disease patients—a perspective review. Adv Park Dis 2(4):113–115

    CrossRef  Google Scholar 

  11. LeMoyne R, Mastroianni T, Grundfest W (2013) Wireless accelerometer configuration for monitoring Parkinson’s disease hand tremor. Adv Park Dis 2(2):62–67

    CrossRef  Google Scholar 

  12. LeMoyne R, Tomycz N, Mastroianni T, McCandless C, Cozza M, Peduto D (2015) Implementation of a smartphone wireless accelerometer platform for establishing deep brain stimulation treatment efficacy of essential tremor with machine learning. In 37th Annual international conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 6772–6775

    Google Scholar 

  13. LeMoyne R, Mastroianni T, Tomycz N, Whiting D, Oh M, McCandless C, Currivan C, Peduto D (2017) Implementation of a multilayer perceptron neural network for classifying deep brain stimulation in ‘On’ and ‘Off’ modes through a smartphone representing a wearable and wireless sensor application. In: 47th Society for Neuroscience annual meeting (featured in Hot Topics; top 1% of abstracts)

    Google Scholar 

  14. LeMoyne R, Mastroianni T, McCandless C, Currivan C, Whiting D, Tomycz N (2018) Implementation of a smartphone as a wearable and wireless accelerometer and gyroscope platform for ascertaining deep brain stimulation treatment efficacy of Parkinson’s disease through machine learning classification. Adv Park Dis 7(2):19–30

    Google Scholar 

  15. Siderowf A, McDermott M, Kieburtz K, Blindauer K, Plumb S, Shoulson I (2002) Test–retest reliability of the unified Parkinson’s disease rating scale in patients with early Parkinson’s disease: results from a multicenter clinical trial. Mov Disord 17(4):758–763

    CrossRef  Google Scholar 

  16. Metman LV, Myre B, Verwey N, Hassin-Baer S, Arzbaecher J, Sierens D, Bakay R (2004) Test–retest reliability of UPDRS-III, dyskinesia scales, and timed motor tests in patients with advanced Parkinson’s disease: an argument against multiple baseline assessments. Mov Disord 19(9):1079–1084

    CrossRef  Google Scholar 

  17. Richards M, Marder K, Cote L, Mayeux R (1994) Interrater reliability of the Unified Parkinson’s Disease Rating Scale motor examination. Mov Disord 9(1):89–91

    CrossRef  Google Scholar 

  18. Fahn S, Elton RL, UPDRS Program Members (1987) Unified Parkinson’s Disease Rating Scale. In: Recent developments in Parkinson’s disease, Vol. 2. Macmillan Healthcare Information, Florham Park, pp 153–163, 293–304.

    Google Scholar 

  19. Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stern MB, Dodel R, Dubois B, Holloway R, Jankovic J, Kulisevsky J, Lang AE, Lees A, Leurgans S, LeWitt PA, Nyenhuis D, Olanow CW, Rascol O, Schrag A, Teresi JA, van Hilten JJ, LaPelle N (2008) Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23(15):2129–2170

    CrossRef  Google Scholar 

  20. Martínez-Martín P, Rodríguez-Blázquez C, Alvarez M, Arakaki T, Arillo VC, Chaná P, Fernández W, Garretto N, Martínez-Castrillo JC, Rodríguez-Violante M, Serrano-Duenas M, Ballesteros D, Rojo-Abuin JM, Chaudhuri KR, Merello M (2015) Parkinson’s disease severity levels and MDS-Unified Parkinson’s Disease Rating Scale. Parkinsonism Relat Disord 21(1):50–54

    CrossRef  Google Scholar 

  21. Bhidayasiri R, Martinez-Martin P (2017) Clinical assessments in Parkinson’s disease: scales and monitoring. Int Rev Neurobiol 132:129–182

    CrossRef  Google Scholar 

  22. Stamatakis J, Ambroise J, Crémers J, Sharei H, Delvaux V, Macq B, Garraux G (2013) Finger tapping clinimetric score prediction in Parkinson’s disease using low-cost accelerometers. Comput Intell Neurosci Article ID 717853:1–13

    CrossRef  Google Scholar 

  23. Habib-ur-Rehman (2000) Diagnosis and management of tremor. Arch Intern Med 160(16):2438–2444

    CrossRef  Google Scholar 

  24. Fahn S, Tolosa E, Marin C (1988) Clinical rating scale for tremor. In: Parkinson’s disease and movement disorders. Urban & Schwarzenberg, Baltimore, pp 225–234

    Google Scholar 

  25. Elble RJ (2016) The essential tremor rating assessment scale. J Neurol Neuromed 1(4):34–38

    CrossRef  Google Scholar 

  26. LeMoyne R, Mastroianni T (2018) Wearable and wireless systems for healthcare I: gait and reflex response quantification. Springer, Singapore

    CrossRef  Google Scholar 

  27. LeMoyne R, Mastroianni T (2017) Smartphone and portable media device: a novel pathway toward the diagnostic characterization of human movement. In: Smartphone from an applied research perspective. InTech, Rijeka, Croatia, pp 1–24

    Google Scholar 

  28. LeMoyne R, Mastroianni T (2017) Wearable and wireless gait analysis platforms: smartphones and portable media devices. In: Wireless MEMS networks and applications. Elsevier, New York, pp 129–152

    CrossRef  Google Scholar 

  29. LeMoyne R, Mastroianni T (2016) Telemedicine perspectives for wearable and wireless applications serving the domain of neurorehabilitation and movement disorder treatment. In: Telemedicine, SMGroup, Dover, Delaware, pp 1–10

    Google Scholar 

  30. LeMoyne R, Mastroianni T (2015) Use of smartphones and portable media devices for quantifying human movement characteristics of gait, tendon reflex response, and Parkinson’s disease hand tremor. In: Mobile health technologies, methods and protocols. Springer, New York, pp 335–358

    Google Scholar 

  31. LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Accelerometers for quantification of gait and movement disorders: a perspective review. J Mech Med Biol 8(2):137–152

    CrossRef  Google Scholar 

  32. LeMoyne RC (2010) Wireless quantified reflex device. Ph.D. Dissertation UCLA

    Google Scholar 

  33. LeMoyne R, Mastroianni T, Coroian C, Grundfest W (2011) Tendon reflex and strategies for quantification, with novel methods incorporating wireless accelerometer reflex quantification devices, a perspective review. J Mech Med Biol 11(3):471–513

    CrossRef  Google Scholar 

  34. LeMoyne R, Mastroianni T, Kale H, Luna J, Stewart J, Elliot S, Bryan F, Coroian C, Grundfest W (2011) Fourth generation wireless reflex quantification system for acquiring tendon reflex response and latency. J Mech Med Biol 11(1):31–54

    CrossRef  Google Scholar 

  35. LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Quantified deep tendon reflex device for response and latency, third generation. J Mech Med Biol 8(4):491–506

    CrossRef  Google Scholar 

  36. LeMoyne R, Dabiri F, Jafari R (2008) Quantified deep tendon reflex device, second generation. J Mech Med Biol 8(1):75–85

    CrossRef  Google Scholar 

  37. LeMoyne R, Jafari R, Jea D (2005) Fully quantified evaluation of myotatic stretch reflex. In: 35th Society for Neuroscience annual meeting

    Google Scholar 

  38. LeMoyne R, Mastroianni T (2017) Implementation of a smartphone wireless gyroscope platform with machine learning for classifying disparity of a hemiplegic patellar tendon reflex pair. J Mech Med Biol 17(6):1750083

    CrossRef  Google Scholar 

  39. LeMoyne R, Kerr W, Zanjani K, Mastroianni T (2014) Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair. J Med Imaging Health Inform 4(1):21–28

    CrossRef  Google Scholar 

  40. LeMoyne R, Mastroianni T (2016) Smartphone wireless gyroscope platform for machine learning classification of hemiplegic patellar tendon reflex pair disparity through a multilayer perceptron neural network. In: Wireless Health (WH) of IEEE, pp 103–108

    Google Scholar 

  41. LeMoyne R, Mastroianni T (2014) Implementation of a smartphone as a wireless gyroscope application for the quantification of reflex response. In: 36th Annual international conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 3654–3657

    Google Scholar 

  42. LeMoyne R, Mastroianni T, Grundfest W, Nishikawa K (2013) Implementation of an iPhone wireless accelerometer application for the quantification of reflex response. In: 35th Annual international conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 4658–4661

    Google Scholar 

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LeMoyne, R., Mastroianni, T., Whiting, D., Tomycz, N. (2019). Traditional Ordinal Strategies for Establishing the Severity and Status of Movement Disorders, Such as Parkinson’s Disease and Essential Tremor. In: Wearable and Wireless Systems for Healthcare II. Smart Sensors, Measurement and Instrumentation, vol 31. Springer, Singapore. https://doi.org/10.1007/978-981-13-5808-1_3

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  • DOI: https://doi.org/10.1007/978-981-13-5808-1_3

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