Skip to main content
SpringerLink
Log in

An IoT-Based System for the Study of Neuropathic Pain in Spinal Cord Injury

  • Conference paper
  • First Online:
Pervasive Computing Technologies for Healthcare (PH 2022)

  • 486 Accesses

Abstract

Neuropathic pain is a difficult condition to treat and would require reliable biomarkers to personalise and optimise treatments. To date, pain levels are mostly measured with subjective scales, but research has shown that electroencephalography (EEG) and heart rate variability (HRV) can be linked to those levels. Internet of Things technology could allow embedding EEG and HRV in easy-to-use systems that patients can use at home in their daily life. We have developed a system for home monitoring that includes a portable EEG device, a tablet application to guide patients through imaginary motor tasks while recording EEG, a wearable HRV sensor and a mobile phone app to report pain levels. We are using this system in a clinical study involving 15 spinal cord injury patients for one month. Preliminary results show that relevant data are being collected, with inter and intra-patients variability for both HRV and pain levels, and that the mobile phone app is perceived as usable, of good quality and useful. However, because of its complexity, the system requires some effort from patients, is sometimes unreliable and the collected EEG signals are not always of the desired quality.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Abrecht, C.R., Nedeljkovic, S.S.: Neuropathic pain. In: Yong, R., Nguyen, M., Nelson, E., Urman, R. (eds.) Pain Medicine, pp. 541–543. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-43133-8_145

    Chapter  Google Scholar 

  2. Anwar, D., Garg, P., Naik, V., Gupta, A., Kumar, A.: Use of portable EEG sensors to detect meditation. In: 2018 10th International Conference on Communication Systems & Networks (COMSNETS), pp. 705–710. IEEE (2018)

    Google Scholar 

  3. Attal, N., Bouhassira, D., Baron, R.: Diagnosis and assessment of neuropathic pain through questionnaires. Lancet Neurol. 17(5), 456–466 (2018)

    Article  Google Scholar 

  4. van den Berg-Emons, R.J., Schasfoort, F.C., de Vos, L.A., Bussmann, J.B., Stam, H.J.: Impact of chronic pain on everyday physical activity. Eur. J. Pain 11(5), 587–593 (2007)

    Article  Google Scholar 

  5. Crichton, N.: Visual analogue scale (VAS). J. Clin. Nurs. 10(5), 706–6 (2001)

    Google Scholar 

  6. Cruccu, G., et al.: EFNS guidelines on neuropathic pain assessment. Eur. J. Neurol. 11(3), 153–162 (2004)

    Article  Google Scholar 

  7. De Pascalis, V., Vecchio, A.: The influence of EEG oscillations, heart rate variability changes, and personality on self-pain and empathy for pain under placebo analgesia. Sci. Rep. 12(1), 1–18 (2022)

    Article  Google Scholar 

  8. Finnerup, N.B., et al.: Pharmacotherapy for neuropathic pain in adults: a systematic review and meta-analysis. Lancet Neurol. 14(2), 162–173 (2015)

    Article  Google Scholar 

  9. Forte, G., Troisi, G., Pazzaglia, M., Pascalis, V.D., Casagrande, M.: Heart rate variability and pain: a systematic review. Brain Sci. 12(2), 153 (2022)

    Article  Google Scholar 

  10. Haanpää, M., et al.: Neupsig guidelines on neuropathic pain assessment. PAIN® 152(1), 14–27 (2011)

    Google Scholar 

  11. Kächele, M., Werner, P., Al-Hamadi, A., Palm, G., Walter, S., Schwenker, F.: Bio-visual fusion for person-independent recognition of pain intensity. In: Schwenker, F., Roli, F., Kittler, J. (eds.) MCS 2015. LNCS, vol. 9132, pp. 220–230. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20248-8_19

    Chapter  Google Scholar 

  12. Karri, J., Zhang, L., Li, S., Chen, Y.T., Stampas, A., Li, S.: Heart rate variability: a novel modality for diagnosing neuropathic pain after spinal cord injury. Front. Physiol. 8, 495 (2017)

    Article  Google Scholar 

  13. Koenig, J., Jarczok, M., Ellis, R., Hillecke, T., Thayer, J.F.: Heart rate variability and experimentally induced pain in healthy adults: a systematic review. Eur. J. Pain 18(3), 301–314 (2014)

    Article  Google Scholar 

  14. Loggia, M.L., Juneau, M., Bushnell, M.C.: Autonomic responses to heat pain: heart rate, skin conductance, and their relation to verbal ratings and stimulus intensity. PAIN® 152(3), 592–598 (2011)

    Google Scholar 

  15. Martin-Payo, R., Carrasco-Santos, S., Cuesta, M., Stoyan, S., Gonzalez-Mendez, X., Fernandez-Alvarez, M.D.M.: Spanish adaptation and validation of the user version of the mobile application rating scale (uMARS). J. Am. Med. Inform. Assoc. 28(12), 2681–2686 (2021)

    Article  Google Scholar 

  16. McGeary, D.D., McGeary, C.A., Gatchel, R.J.: A comprehensive review of telehealth for pain management: where we are and the way ahead. Pain Pract. 12(7), 570–577 (2012)

    Article  Google Scholar 

  17. Mohamed, A.H.H., Tawfik, H., Al-Jumeily, D., Norton, L.: MoHTAM: a technology acceptance model for mobile health applications. In: 2011 Developments in E-systems Engineering, pp. 13–18. IEEE (2011)

    Google Scholar 

  18. Möltner, A., Hölzl, R., Strian, F.: Heart rate changes as an autonomic component of the pain response. Pain 43(1), 81–89 (1990)

    Article  Google Scholar 

  19. Mussigmann, T., Bardel, B., Lefaucheur, J.P.: Resting-state electroencephalography (EEG) biomarkers of chronic neuropathic pain. a systematic review. NeuroImage 119351 (2022)

    Google Scholar 

  20. Natarajan, A., Pantelopoulos, A., Emir-Farinas, H., Natarajan, P.: Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study. Lancet Digit. Health 2(12), e650–e657 (2020)

    Article  Google Scholar 

  21. Prada, E.J.A.: The internet of things (IoT) in pain assessment and management: an overview. Inform. Med. Unlock. 18, 100298 (2020)

    Google Scholar 

  22. Rajesh, M., Muthu, J.S., Suseela, G.: iPainRelief-a pain assessment and management app for a smart phone implementing sensors and soft computing tools. In: 2013 International Conference on Information Communication and Embedded Systems (ICICES), pp. 434–441. IEEE (2013)

    Google Scholar 

  23. Rost, S., Van Ryckeghem, D.M., Koval, P., Sütterlin, S., Vögele, C., Crombez, G.: Affective instability in patients with chronic pain: a diary approach. Pain 157(8), 1783–1790 (2016)

    Article  Google Scholar 

  24. Salvi, D., Lee, J., Velardo, C., Goburdhun, R.A., Tarassenko, L.: Mobistudy: an open mobile-health platform for clinical research. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 918–921. IEEE (2019)

    Google Scholar 

  25. Salvi, D., Olsson, C.M., Ymeri, G., Carrasco-Lopez, C., Tsang, K.C., Shah, S.A.: Mobistudy: mobile-based, platform-independent, multi-dimensional data collection for clinical studies. In: 11th International Conference on the Internet of Things, pp. 219–222 (2021)

    Google Scholar 

  26. Samandari, R.: Integration of bluetooth sensors in a windows-based research platform. Bachelor’s thesis, Malmö University (2021)

    Google Scholar 

  27. Siddall, P.J., McClelland, J.M., Rutkowski, S.B., Cousins, M.J.: A longitudinal study of the prevalence and characteristics of pain in the first 5 years following spinal cord injury. Pain 103(3), 249–257 (2003)

    Article  Google Scholar 

  28. Spenkelink, C., Hutten, M.M., Hermens, H., Greitemann, B.O.: Assessment of activities of daily living with an ambulatory monitoring system: a comparative study in patients with chronic low back pain and nonsymptomatic controls. Clin. Rehabil. 16(1), 16–26 (2002)

    Article  Google Scholar 

  29. Sundararaman, L.V., Edwards, R.R., Ross, E.L., Jamison, R.N.: Integration of mobile health technology in the treatment of chronic pain: a critical review. Regional Anesth. Pain Med. 42(4), 488–498 (2017)

    Article  Google Scholar 

  30. Thurnheer, S.E., Gravestock, I., Pichierri, G., Steurer, J., Burgstaller, J.M.: Benefits of mobile apps in pain management: systematic review. JMIR Mhealth Uhealth 6(10), e11231 (2018)

    Google Scholar 

  31. Tracy, L.M., Ioannou, L., Baker, K.S., Gibson, S.J., Georgiou-Karistianis, N., Giummarra, M.J.: Meta-analytic evidence for decreased heart rate variability in chronic pain implicating parasympathetic nervous system dysregulation. Pain 157(1), 7–29 (2016)

    Article  Google Scholar 

  32. Van Gorp, S., Kessels, A., Joosten, E., Van Kleef, M., Patijn, J.: Pain prevalence and its determinants after spinal cord injury: a systematic review. Eur. J. Pain 19(1), 5–14 (2015)

    Article  Google Scholar 

  33. Van Weering, M., Vollenbroek-Hutten, M., Tönis, T., Hermens, H.: Daily physical activities in chronic lower back pain patients assessed with accelerometry. Eur. J. Pain 13(6), 649–654 (2009)

    Article  Google Scholar 

  34. Vanneste, S., De Ridder, D.: Chronic pain as a brain imbalance between pain input and pain suppression. Brain Commun. 3(1), fcab014 (2021)

    Google Scholar 

  35. Vuckovic, A., Gallardo, V.J.F., Jarjees, M., Fraser, M., Purcell, M.: Prediction of central neuropathic pain in spinal cord injury based on EEG classifier. Clin. Neurophysiol. 129(8), 1605–1617 (2018)

    Article  Google Scholar 

  36. Xhyheri, B., Manfrini, O., Mazzolini, M., Pizzi, C., Bugiardini, R.: Heart rate variability today. Prog. Cardiovasc. Dis. 55(3), 321–331 (2012)

    Article  Google Scholar 

  37. Xu, J., Zhong, B.: Review on portable EEG technology in educational research. Comput. Hum. Behav. 81, 340–349 (2018)

    Article  Google Scholar 

  38. Yang, G., et al.: IoT-based remote pain monitoring system: from device to cloud platform. IEEE J. Biomed. Health Inform. 22(6), 1711–1719 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

The work presented in this paper has been partially funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 101030384, by the Swedish Knowledge Foundation through the Internet of Things and People research center, and thanks to sponsorship from Bitbrain and Sony. We would like to thank Rohan Samandari for his contributions to the development of the tablet application.

Author information

Authors and Affiliations

  1. Internet of Things and People, Malmö University, Malmö, Sweden

    Dario Salvi, Gent Ymeri, Carl Magnus Olsson & Carmen Carrasco-Lopez

  2. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain

    Daniel Jimeno

  3. FENNSI Group, National Hospital for Paraplegics, Toledo, Spain

    Vanesa Soto-León & Yolanda Pérez-Borrego

Authors
  1. Dario Salvi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gent Ymeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Jimeno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vanesa Soto-León
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yolanda Pérez-Borrego
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Carl Magnus Olsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Carmen Carrasco-Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dario Salvi .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  2. Centre for Research and Technology Hellas, Thessaloniki, Greece

    Andreas Triantafyllidis

Rights and permissions

Reprints and permissions

Copyright information

© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Salvi, D. et al. (2023). An IoT-Based System for the Study of Neuropathic Pain in Spinal Cord Injury. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34586-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34585-2

  • Online ISBN: 978-3-031-34586-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation