Skip to main content

Deep Brain Stimulation for the Treatment of Movement Disorder Regarding Parkinson’s Disease and Essential Tremor with Device Characterization

  • Chapter
  • First Online:
Wearable and Wireless Systems for Healthcare II

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

Abstract

Deep brain stimulation has provided an efficacious alternative for the treatment of progressive neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor, for more than a quarter of a century. This intervention strategy offers an adjustable and even reversible therapy by contrast to the permanency of lesion inducing neurosurgery, especially if medication has been deemed intractable. Furthermore, deep brain stimulation has been affirmed as a long-term means of suppressing movement disorder symptoms, although the foundational mechanisms remain to be conclusively ascertained. Target selection is specific to the type of neurodegenerative movement disorder diagnosed, and primary risks pertain to surgery and adverse effects resultant from the activation of stimulation. The general aspects that comprise the deep brain stimulation system are discussed from a system-level perspective, such as the implantable pulse generator, battery, connecting wire, and electrode lead. Even after the expert implantation of a deep brain stimulation system, the acquisition of an optimal parameter configuration presents a rather daunting and time-consuming process even for the highly talented and skilled clinician. The sheer quantity of permutations for optimizing parameters, such as polarity, amplitude of stimulation, rate of stimulation, and pulse width, presents a labor-intensive endeavor. The parameter configuration and operation of the deep brain stimulation system are modulated by the clinician programmer and patient programmer. Future concepts for deep brain stimulation are discussed, such as closed-loop acquisition of configuration parameters. Foundational to the perspective of deep brain stimulation optimization is the application of wearable and wireless systems, such as the smartphone, for objectively quantified feedback of movement disorder status. These envisioned technology evolutions advocate the prominence of Network Centric Therapy for the treatment of neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Williams R (2010) Alim-Louis Benabid: stimulation and serendipity. Lancet Neurol 9(12):1152

    Article  Google Scholar 

  2. Miocinovic S, Somayajula S, Chitnis S, Vitek JL (2013) History, applications, and mechanisms of deep brain stimulation. JAMA Neurol 70(2):163–171

    Article  Google Scholar 

  3. Benabid AL, Pollak P, Louveau A, Henry S, de Rougemont J (1987) Combined (thalamotomy and stimulation) stereotactic surgery of the VIM thalamic nucleus for bilateral Parkinson’s disease. Appl Neurophysiol 50(1–6):344–346

    Google Scholar 

  4. Rehncrona S, Johnels B, Widner H, Törnqvist AL, Hariz M, Sydow O (2003) Long-term efficacy of thalamic deep brain stimulation for tremor: double-blind assessments. Mov Disord 18(2):163–170

    Article  Google Scholar 

  5. Sydow O, Thobois S, Alesch F, Speelman JD (2003) Multicentre European study of thalamic stimulation in essential tremor: a six year follow up. J Neurol Neurosurg Psychiatry 74(10):1387–1391

    Article  Google Scholar 

  6. Krack P, Batir A, Van Blercom N, Chabardes S, Fraix V, Ardouin C, Koudsie A, Limousin PD, Benazzouz A, LeBas JF, Benabid AL, Pollak P (2003) Five-year follow-up of bilateral stimulation of the subthalamic nucleus in advanced Parkinson’s disease. N Engl J Med 349(20):1925–1934

    Article  Google Scholar 

  7. Lyons KE, Koller WC, Wilkinson SB, Pahwa R (2001) Long term safety and efficacy of unilateral deep brain stimulation of the thalamus for parkinsonian tremor. J Neurol Neurosurg Psychiatry 71(5):682–684

    Article  Google Scholar 

  8. Benabid AL, Benazzous A, Pollak P (2002) Mechanisms of deep brain stimulation. Mov Disord 17(S3):S73–S74

    Article  Google Scholar 

  9. Yu H, Neimat JS (2008) The treatment of movement disorders by deep brain stimulation. Neurotherapeutics 5(1):26–36

    Article  Google Scholar 

  10. Hassler R (1959) Anatomy of the thalamus. In: Introduction to stereotaxis with an atlas of the human brain. Thieme, Stuttgart, pp 230–290

    Google Scholar 

  11. Amon A, Alesch F (2017) Systems for deep brain stimulation: review of technical features. J Neural Transm 124(9):1083–1091

    Article  Google Scholar 

  12. Volkmann J, Moro E, Pahwa R (2006) Basic algorithms for the programming of deep brain stimulation in Parkinson’s disease. Mov Disord 21(S14):S284–S289

    Article  Google Scholar 

  13. Isaias IU, Tagliati M (2008) Deep brain stimulation programming for movement disorders. In: Deep brain stimulation in neurological and psychiatric disorders. Springer, New York, pp 361–397

    Chapter  Google Scholar 

  14. 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 

  15. 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 

  16. 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 

  17. 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 

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

    Google Scholar 

  19. 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

    Chapter  Google Scholar 

  20. 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 

  21. 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 

  22. Hariz M (2017) My 25 stimulating years with DBS in Parkinson’s disease. J Park Dis 7(s1):S33–S41

    Google Scholar 

  23. Fang JY, Tolleson C (2017) The role of deep brain stimulation in Parkinson’s disease: an overview and update on new developments. Neuropsychiatr Dis Treat 13:723–732

    Article  Google Scholar 

  24. Benabid AL (2003) Deep brain stimulation for Parkinson’s disease. Curr Opin Neurobiol 13(6):696–706

    Article  Google Scholar 

  25. Starr PA, Vitek JL, Bakay RA (1998) Deep brain stimulation for movement disorders. Neurosurg Clin N Am 9(2):381–402

    Article  Google Scholar 

  26. Pollak P, Benabid AL, Gross CH, Gao DM, Laurent A, Benazzouz A, Hoffmann D, Gentil M, Perret J (1993) Effets de la stimulation du noyau sous-thalamique dans la maladie de Parkinson. Rev Neurol (Paris) 149(3):175–176

    Google Scholar 

  27. Kopell BH, Rezai AR, Chang JW, Vitek JL (2006) Anatomy and physiology of the basal ganglia: implications for deep brain stimulation for Parkinson’s disease. Mov Disord 21(S14):S238–S246

    Article  Google Scholar 

  28. Schüpbach W, Chastan N, Welter M, Houeto J, Mesnage V, Bonnet A, Czernecki V, Maltête D, Hartmann A, Mallet L, Pidoux B, Dormont D, Navarro S, Cornu P, Mallet A, Agid Y (2005) Stimulation of the subthalamic nucleus in Parkinson’s disease: a 5 year follow up. J Neurol Neurosurg Psychiatry 76(12):1640–1644

    Google Scholar 

  29. Siegfried J, Lippitz B (1994) Bilateral chronic electrostimulation of ventroposterolateral pallidum: a new therapeutic approach for alleviating all parkinsonian symptoms. Neurosurgery 35(6):1126–1130

    Article  Google Scholar 

  30. Butson CR, McIntyre CC (2008) Current steering to control the volume of tissue activated during deep brain stimulation. Brain Stimul 1(1):7–15

    Article  Google Scholar 

  31. Benabid AL, Pollak P, Gao D, Hoffmann D, Limousin P, Gay E, Payen I, Benazzouz A (1996) Chronic electrical stimulation of the ventralis intermedius nucleus of the thalamus as a treatment of movement disorders. J Neurosurg 84(2):203–214

    Article  Google Scholar 

  32. Hartmann CJ, Wojtecki L, Vesper J, Volkmann J, Groiss SJ, Schnitzler A, Sudmeyer M (2015) Long-term evaluation of impedance levels and clinical development in subthalamic deep brain stimulation for Parkinson’s disease. Parkinsonism Relat Disord 21(10):1247–1250

    Article  Google Scholar 

  33. Bronstein JM, Tagliati M, McIntyre C, Chen R, Cheung T, Hargreaves EL, Israel Z, Moffitt M, Montgomery EB, Stypulkowski P, Shils J, Denison T, Vitek J, Volkman J, Wertheimer J, Okun MS (2015) The rationale driving the evolution of deep brain stimulation to constant-current devices. Neuromodulation 18(2):85–89

    Article  Google Scholar 

  34. LeMoyne R, Heerinckx F, Aranca T, De Jager R, Zesiewicz T, Saal HJ (2016) Wearable body and wireless inertial sensors for machine learning classification of gait for people with Friedreich’s ataxia. In: IEEE 13th International conference on wearable and implantable Body Sensor Networks (BSN), pp 147–151

    Google Scholar 

  35. LeMoyne R, Mastroianni T (2018) Bluetooth inertial sensors for gait and reflex response quantification with perspectives regarding cloud computing and the Internet of Things. In: Wearable and wireless systems for healthcare I: gait and reflex response quantification. Springer, Singapore, pp 95–103

    Google Scholar 

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

    Book  Google Scholar 

  37. Pretto T (2007) Deep brain stimulation. Neurologist 13(2):103–104

    Article  Google Scholar 

  38. Panisset M, Picillo M, Jodoin N, Poon YY, Valencia-Mizrachi A, Fasano A, Munhoz R, Honey CR (2017) Establishing a standard of care for deep brain stimulation centers in Canada. Can J Neurol Sci 44(2):132–138

    Article  Google Scholar 

  39. Schwalb JM, Hamani C (2008) The history and future of deep brain stimulation. Neurotherapeutics 5(1):3–13

    Article  Google Scholar 

  40. Sun FT, Morrell MJ (2014) Closed-loop neurostimulation: the clinical experience. Neurotherapeutics 11(3):553–563

    Article  Google Scholar 

  41. Priori A, Foffani G, Rossi L, Marceglia S (2013) Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. Exp Neurol 245:77–86

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

LeMoyne, R., Mastroianni, T., Whiting, D., Tomycz, N. (2019). Deep Brain Stimulation for the Treatment of Movement Disorder Regarding Parkinson’s Disease and Essential Tremor with Device Characterization. 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_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-5808-1_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5807-4

  • Online ISBN: 978-981-13-5808-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics