fNIRS Approach to Pain Assessment for Non-verbal Patients

  • Raul Fernandez Rojas
  • Xu Huang
  • Julio Romero
  • Keng-Liang Ou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)

Abstract

The absence of verbal communication in some patients (e.g., critically ill, suffering from advanced dementia) difficults their pain assessment due to the impossibility to self-report pain. Functional near-infrared spectroscopy (fNIRS) is a non-invasive technology that has showed promising results in assessing cortical activity in response to painful stimulation. In this study, we used fNIRS signals to predict the state of pain in humans using machine learning methods. Eighteen healthy subjects were stimulated using thermal stimuli with a thermode, while their cortical activity was recorded using fNIRS. Bag-of-words (BoW) model was used to represent each fNIRS time series. The effect of different step sizes, window lengths, and codebook sizes was investigated to improve computational cost and generalization. In addition, we explored the effect of choosing different features as neurological biomarkers in three different domains: time, frequency, and time-frequency (wavelet). Classification on the histogram representation was performed using K-nearest neighbours (K-NN). The performance is evaluated by using leave-one-out cross validation and with different nearest neighbours. The results showed that wavelet-based features produced the highest accuracy (\(88.33\%\)) to distinguish between heat and cold pain while discriminate between low and high pain. It is possible to use fNIRS to assess pain in response to four types of thermal pain. However, future research is needed for the assessment of pain in clinical settings.

Keywords

Haemodynamic Multiclass Pain Time series Neural Brain 

References

  1. 1.
    Brown, J.E., Chatterjee, N., Younger, J., Mackey, S.: Towards a physiology-based measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation. PLoS ONE 6(9), e24124 (2011)CrossRefGoogle Scholar
  2. 2.
    Cowen, R., Stasiowska, M.K., Laycock, H., Bantel, C.: Assessing pain objectively: the use of physiological markers. Anaesthesia 70(7), 828–847 (2015)CrossRefGoogle Scholar
  3. 3.
    Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endowment 1(2), 1542–1552 (2008)CrossRefGoogle Scholar
  4. 4.
    Gram, M., Graversen, C., Olesen, A.E., Drewes, A.: Machine learning on encephalographic activity may predict opioid analgesia. Eur. J. Pain 19(10), 1552–1561 (2015)CrossRefGoogle Scholar
  5. 5.
    Herr, K., Coyne, P.J., McCaffery, M., Manworren, R., Merkel, S.: Pain assessment in the patient unable to self-report: position statement with clinical practice recommendations. Pain Manag. Nurs. 12(4), 230–250 (2011)CrossRefGoogle Scholar
  6. 6.
    Kirilina, E., Yu, N., Jelzow, A., Wabnitz, H., Jacobs, A.M., Tachtsidis, I.: Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex. Front. Hum. Neurosci. 7, 864 (2013)Google Scholar
  7. 7.
    Lin, J., Li, Y.: Finding structural similarity in time series data using bag-of-patterns representation. In: Winslett, M. (ed.) SSDBM 2009. LNCS, vol. 5566, pp. 461–477. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02279-1_33 CrossRefGoogle Scholar
  8. 8.
    Pourshoghi, A., Zakeri, I., Pourrezaei, K.: Application of functional data analysis in classification and clustering of functional near-infrared spectroscopy signal in response to noxious stimuli. J. Biomed. Opt. 21(10), 101411 (2016)CrossRefGoogle Scholar
  9. 9.
    Ranger, M., Gélinas, C.: Innovating in pain assessment of the critically ill: exploring cerebral near-infrared spectroscopy as a bedside approach. Pain Manag. Nurs. 15(2), 519–529 (2014)CrossRefGoogle Scholar
  10. 10.
    Rolke, R., Baron, R., Maier, C.A., Tölle, T., Treede, R.D., Beyer, A., Binder, A., Birbaumer, N., Birklein, F., Bötefür, I., et al.: Quantitative sensory testing in the german research network on neuropathic pain (DFNS): standardized protocol and reference values. Pain 123(3), 231–243 (2006)CrossRefGoogle Scholar
  11. 11.
    Wager, T.D., Atlas, L.Y., Lindquist, M.A., Roy, M., Woo, C.W., Kross, E.: An fMRI-based neurologic signature of physical pain. New Engl. J. Med. 368(15), 1388–1397 (2013)CrossRefGoogle Scholar
  12. 12.
    Yamamoto, T., Kato, T.: Paradoxical correlation between signal in functional magnetic resonance imaging and deoxygenated haemoglobin content in capillaries: a new theoretical explanation. Phys. Med. Biol. 47(7), 1121 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Raul Fernandez Rojas
    • 1
  • Xu Huang
    • 1
  • Julio Romero
    • 1
  • Keng-Liang Ou
    • 2
    • 3
    • 4
  1. 1.Human-Centred Technology Research Centre, ESTEM FacultyUniversity of Canberra, ACTCanberraAustralia
  2. 2.Department of DentistryTaipei Medical University HospitalTaipeiTaiwan
  3. 3.Department of DentistryTaipei Medical University-Shuang Ho HospitalNew Taipei CityTaiwan
  4. 4.3D Global Biotech Inc.New Taipei CityTaiwan

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