Scope of physiological and behavioural pain assessment techniques in children – a review
- Author(s): Saranya Devi Subramaniam 1 ; Brindha Doss 1 ; Lakshmi Deepika Chanderasekar 2 ; Aswini Madhavan 1 ; Antony Merlin Rosary 2
-
-
View affiliations
-
Affiliations:
1:
Department of Biomedical Engineering , PSG College of Technology , Coimbatore 641004 , India ;
2: Department of Electronics & Communication Engineering , PSG College of Technology , Coimbatore, 641004 , India
-
Affiliations:
1:
Department of Biomedical Engineering , PSG College of Technology , Coimbatore 641004 , India ;
- Source:
Volume 5, Issue 4,
August
2018,
p.
124 – 129
DOI: 10.1049/htl.2017.0108 , Online ISSN 2053-3713
Pain is an unpleasant subjective experience. At present, clinicians are using self-report or pain scales to recognise and monitor pain in children. However, these techniques are not efficient to observe the pain in children having cognitive disorder and also require highly skilled observers to measure pain. Using these techniques it is also difficult to choose the analgesic drug dosages to the patients after surgery. Thus, this conceptual work explains the demand for automatic coding techniques to evaluate pain and also it documents some evidence of techniques that act as an alternative approach for objectively determining pain in children. In this review, some good indicators of pain in children are explained in detail; they are facial expressions from an RGB image, thermal image and also feature from well proven physiological signals such as electrocardiogram, skin conductance, body temperature, surgical pleth index, pupillary reflex dilation, analgesia nociception index, photoplethysmography, perfusion index etc.
Inspec keywords: patient diagnosis; biomedical measurement; cognition; neurophysiology; paediatrics
Other keywords: electrocardiogram; surgical pleth index; body temperature; behavioural pain assessment techniques; perfusion index; analgesia nociception index; RGB image; skin conductance; pupillary reflex dilation; analgesic drug dosages; physiological pain assessment techniques; photoplethysmography; children; cognitive disorder
Subjects: Biomedical measurement and imaging; Biophysics of neurophysiological processes; Electrodiagnostics and other electrical measurement techniques
References
-
-
1)
-
25. Lu, G., Yuan, L., Li, X., et al: ‘Facial expression recognition of pain in neonates’. Proc. Int. Conf. on Computer Science Software Engineering (CSSE), 2008, vol. 1, pp. 756–759.
-
-
2)
-
35. Loggia, M., Bushnell, M.C., Juneau, M.: ‘Autonomic responses to heat pain: heart rate, skin conductance, and their relation to verbal ratings and stimulus intensity’, Pain, 2011, 152, pp. 592–598 (doi: 10.1016/j.pain.2010.11.032).
-
-
3)
-
22. Patil, R.A., Sahula, V., Mandal, A.S.: ‘Facial expression recognition in image sequences using active shape model and support vector machine’. 2011 5th UKSim European Symp. on Computer Modeling and Simulation (EMS), 2011, pp. 168–173.
-
-
4)
-
40. Boselli, E., Jeanne, M.: ‘Analgesia/nociception index for the assessment of acute postoperative pain’, Br. J. Anaesth., 2014, 112, (5), pp. 936–937 (doi: 10.1093/bja/aeu116).
-
-
5)
-
48. Bardhan, S., Bhowmik, M.K., Nath, S., et al: ‘A review on inflammatory pain detection in human body through infrared image analysis’. Int. Symp. on Advanced Computing and Communication (ISACC), 2015.
-
-
6)
-
31. Hong, X., Zhao, G., Zafeiriou, S., et al: ‘Capturing correlations of local features for image representation’, Neurocomputing, 2016, 184, pp. 99–106 (doi: 10.1016/j.neucom.2015.07.134).
-
-
7)
-
28. Naufal Mansor, M., Rejab, M.N.: ‘Phase congruency image and sparse classifier for newborn classifying pain state’. Proc. 2013 IEEE Int. Conf. on Control System, Computing and Engineering (ICCSCE), 2013, pp. 450–454.
-
-
8)
-
14. Del Bene, V.E.: ‘Temperature’, in Walker, H.K., Wall, W.D., Hurst, J.W. (Eds.): ‘Clinical methods: the history, physical, and laboratory examinations’ (Butterworth-Heinemann Ltd, Boston, MA, USA, 1990, 3rd edn.), pp. 990–993.
-
-
9)
-
10. Frampton, C.L., Hughes-Webb, P.: ‘The measurement of pain’, Clin. Oncol., 2011, 23, (6), pp. 381–386 (doi: 10.1016/j.clon.2011.04.008).
-
-
10)
-
10. Viola, P., Jones, M.: ‘Robust real-time face detection’, Int. J. Comput. Vis., 2004, 2, (57), pp. 137–154 (doi: 10.1023/B:VISI.0000013087.49260.fb).
-
-
11)
-
26. Niu, Z., Qiu, X.: ‘Facial expression recognition based on weighted principal component analysis and support vector machines’. Third Int. Conf. on Advanced Computer Theory and Engineering (ICACTE), 2010, vol. 3, pp. 174–178.
-
-
12)
-
1. Rourke, D.O.: ‘The measurement of pain in infants, children, and adolescents: from policy to practice’, J. Am. Phys. Ther. Assoc., 2004, 84, pp. 560–570.
-
-
13)
-
50. Kachele, M., Thiam, P., Amirian, M., et al: ‘Multimodal data fusion for person independent, continuous estimation of pain intensity’, Engineering Applications of Neural Networks (EANN), Rhodes, Greece, September 2015, pp. 275–285.
-
-
14)
-
51. Walter, S., Gruss, S., Ehleiter, H., et al: ‘The BioVid heat pain database: data for the advancement and systematic validation of an automated pain recognition’. 2013 IEEE Int. Conf. on Cybernetics (CYBCONF), June 2013, pp. 128–131.
-
-
15)
-
19. Kalita, J., Das, K.: ‘Recognition of facial expression using eigenvector based distributed features and Euclidean distance based decision making technique’, Int. J. Adv. Comput. Sci. Appl., 2013, 4, (2), pp. 196–202.
-
-
16)
-
20. Punitha, A., Kalaiselvigeetha, M.: ‘Texture based emotion recognition from facial expression using support vector machine’, Int. J. Comput. Appl., 2013, 80, (5), pp. 1–5.
-
-
17)
-
45. Hooshmand, H., Hashmi, M., Phillips, E.M.: ‘Infrared thermal imaging as a tool in pain management – an 11 year study, part I of II’, Thermol. Int., 2001, 11, pp. 53–65.
-
-
18)
-
47. Merla, A., Ciuffolo, F., Attilio, M.D., et al: ‘Functional infrared imaging in the diagnosis of the myofascial pain’. Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 2004, vol. 2, pp. 1188–1191.
-
-
19)
-
7. McGrath, P.A., Gillespie, J.: ‘Pain assessment in children and adolescents’, in Turk, D.C., Melzack, R. (Eds.): ‘Handbook of pain assessment’ (Guildford Press, New York, NY, USA, 2001), no. 2, pp. 97–118.
-
-
20)
-
42. Aissou, M., Snauwaert, A., Dupuis, C., et al: ‘Objective assessment of the immediate postoperative analgesia using pupillary reflex measurement: a prospective and observational study’, Anesthesiology, 2012, 116, (5), pp. 1006–1012 (doi: 10.1097/ALN.0b013e318251d1fb).
-
-
21)
-
9. Bartlett, M.S., Littlewort, G.C., Frank, M.G., et al: ‘Automatic decoding of facial movements reveals deceptive pain expressions’, Curr. Biol., 2014, 24, (7), pp. 738–743 (doi: 10.1016/j.cub.2014.02.009).
-
-
22)
-
30. Zamzami, G., Ruiz, G., Goldgof, D., et al: ‘Pain assessment in infants: towards spotting pain expression based on infants’ facial strain’. 2015 11th IEEE Int. Conf. and Workshops on Automatic Face and Gesture Recognition (FG), 2015, vol. 5, pp. 1–5.
-
-
23)
-
43. Jang, E., Park, B., Kim, S., et al: ‘Classification of human emotions from physiological signals using machine learning algorithms: recognition of pain, boredom, and surprise emotions’. The Sixth Int. Conf. on Advances in Computer–Human Interactions (ACHI 2013), 2013, pp. 395–400.
-
-
24)
-
39. Sabourdin, N., Arnaout, M., Louvet, N., et al: ‘Pain monitoring in anesthetized children: first assessment of skin conductance and analgesia-nociception index at different infusion rates of remifentanil’, Paediatr. Anaesth., 2013, 23, (2), pp. 149–155 (doi: 10.1111/pan.12071).
-
-
25)
-
34. Owens, M.E., Todt, E.H.: ‘Pain in infancy: neonatal reaction to a heel lance’, Pain, 1984, 20, (1), pp. 77–86 (doi: 10.1016/0304-3959(84)90813-3).
-
-
26)
-
29. Prkachin, K.M., Solomon, P.E.: ‘The structure, reliability and validity of pain expression: evidence from patients with shoulder pain’, Pain, 2008, 139, (2), pp. 267–274 (doi: 10.1016/j.pain.2008.04.010).
-
-
27)
-
3. McGrath, P.J., Unruh, A.M.: ‘Measurement and assessment of paediatric pain’ in Wall, P.D., Melzack, R. (Eds.): ‘Textbook of pain’ (Churchill Livingstone, Edinburgh, Scotland, 1999, 4th edn.), pp. 371–384..
-
-
28)
-
21. Schiavenato, M., Byers, J.F., Scovanner, P., et al: ‘Neonatal pain facial expression: evaluating the primal face of pain’, Pain, 2008, 138, (2), pp. 460–471 (doi: 10.1016/j.pain.2008.07.009).
-
-
29)
-
13. Cohn, J.F., Ambadar, Z., Ekman, P.: ‘Observer-based measurement of facial expression with the facial action coding system’ in ‘Handbook of emotion elicitation and assessment’ (2005), pp. 203–221.
-
-
30)
-
17. Yow, K.C., Cipolla, R.: ‘Feature based human face detection’, Image Vis. Comput., 1997, 15, (9), pp. 713–735 (doi: 10.1016/S0262-8856(97)00003-6).
-
-
31)
-
46. Frize, M., Herry, C., Scales, N.: ‘Processing thermal images to detect breast cancer and assess pain’. Proc. IEEE/EMBS Region 8 Int. Conf. on Information Technology Applications in Biomedicine (ITAB), 2003, pp. 234–237.
-
-
32)
-
11. McGrath, P.J., Johnson, G., Goodman, J.T., et al: ‘CHEOPS: a behavioural scale for rating postoperative pain in children’ inFields, H.L., Dubner, R., Cervero, F. (Eds.): ‘Proceedings of the fourth world congress on pain, advances in pain research and therapy’, vol. 9 (Raven Press, New York, NY, 1985), pp. 395–401.
-
-
33)
-
52. Zamzmi, G., Pai, C., Goldgof, D., et al: ‘An approach for automated multimodal analysis of infants’ pain’. 23rd Int. Conf. on Pattern Recognition (ICPR), 2016, pp. 4143–4148.
-
-
34)
-
53. Petroni, M., Malowany, A.S., Johnston, C.C., et al: ‘Classification of infant cry vocalizations using artificial neural networks (ANNs)’. Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 1995, pp. 3475–3478.
-
-
35)
-
6. de Jesus, J.A.L., Tristao, R.M., Storm, H., et al: ‘Heart rate, oxygen saturation, and skin conductance: a comparison study of acute pain in Brazilian newborns’, Conf. Proc. IEEE. Eng. Med. Biol. Soc., 2011, 2011, pp. 1875–1879.
-
-
36)
-
38. Thee, C., Ilies, C., Gruenewald, M., et al: ‘Reliability of the surgical pleth index for assessment of postoperative pain’, Eur. J. Anaesthesiol., 2015, 32, (1), pp. 44–48 (doi: 10.1097/EJA.0000000000000095).
-
-
37)
-
5. Ekman, P., Friesen, W.V., Hager, J.C.: ‘Facial action coding system investigator's guide’ (Consultant Pschologists Press, Salt Lake City, UT, 2002).
-
-
38)
-
44. Lopez-Martinez, D., Picard, R.: ‘Multi-task neural networks for personalized pain recognition from physiological signals’. Seventh Int. Conf. on Affective Computing and Intelligent Interaction Workshops and Demos, 2017, pp. 3–6.
-
-
39)
-
54. Lucey, P., Cohn, J.F., Prkachin, K.M., et al: ‘Painful data: the UNBC-McMaster shoulder pain expression archive database’. 2011 IEEE Int. Conf. on Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011, pp. 57–64.
-
-
40)
-
4. Villarroel, M., Guazzi, A., Jorge, J., et al: ‘Continuous non-contact vital sign monitoring in neonatal intensive care unit’, Healthc. Technol. Lett., 2014, 1, (3), pp. 87–91 (doi: 10.1049/htl.2014.0077).
-
-
41)
-
36. Ye, J., Lee, K., Lin, J., et al: ‘Observing continuous change in heart rate variability and photoplethysmography-derived parameters during the process of pain production/relief with thermal stimuli’, J. Pain Res., 2017, pp. 527–533 (doi: 10.2147/JPR.S129287).
-
-
42)
-
12. Sayette, M.A., Cohn, J.F., Wertz, J.M., et al: ‘A psychometric evaluation of the facial action coding system for assessing spontaneous expression’, J. Nonverbal Behav., 1997, 25, pp. 167–186 (doi: 10.1023/A:1010671109788).
-
-
43)
-
37. Ledowski, T., Ang, B., Schmarbeck, T., et al: ‘Monitoring of sympathetic tone to assess postoperative pain: skin conductance vs surgical stress index’, Anaesthesia, 2008, 64, pp. 727–731 (doi: 10.1111/j.1365-2044.2008.05834.x).
-
-
44)
-
8. Champion, G.D., Good enough, B., von Baeyer, C., et al: ‘Measurement of pain by self-report’ in Finley, G., McGrath, P. (Eds.): ‘Measurement of pain in infants and children’ (IASP Press, Seattle, WA, USA, 1998), pp. 123–160.
-
-
45)
-
27. Naufal Mansor, M., Rejab, M.N.: ‘Infant pain recognition system with GLCM features and GANN under unstructured lighting condition’. Proc. 2013 IEEE Int. Conf. on Control System, Computing and Engineering (ICCSCE), 2013, pp. 243–248.
-
-
46)
-
16. Lee, Y.-B., Lee, S.: ‘Robust face detection based on knowledge-directed specification of bottom-up saliency’, ETRI J., 2011, 33, (4), pp. 600–610 (doi: 10.4218/etrij.11.1510.0123).
-
-
47)
-
24. Yuan, L., Bao, F.S., Lu, G.: ‘Recognition of neonatal facial expressions of acute pain using boosted Gabor features’. Proc. Int. Conf. on Tools with Artificial Intelligence, ICTAI, 2008, vol. 2, pp. 473–476.
-
-
48)
-
2. Kirshner, B., Guyatt, G.: ‘A methodological framework for assessing health indices’, J. Chronic Dis., 1985, 38, pp. 27–36 (doi: 10.1016/0021-9681(85)90005-0).
-
-
49)
-
32. Zhou, J., Hong, X.: ‘Recurrent convolutional neural network regression for continuous pain intensity estimation in video’. Proc. IEEE Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–11.
-
-
50)
-
41. Constant, I., Nghe, M.C., Boudet, L., et al: ‘Reflex pupillary dilatation in response to skin incision and alfentanil in children anaesthetized with sevoflurane: a more sensitive measure of noxious stimulation than the commonly used variables’, Br. J. Anaesth., 2006, 96, (5), pp. 614–619 (doi: 10.1093/bja/ael073).
-
-
51)
-
4. Sweet, S.D., McGrath, P.J.: ‘Physiological measures of pain’ in ‘Measurement of pain in infants and children’ (IASP Press, Seattle, WA, USA, 1998, 2nd edn.), pp. 59–81.
-
-
52)
-
23. Mansor, M.N., Syam, S.H.F., Rejab, M.N., et al: ‘AR model for infant pain anxiety recognition using fuzzy k-NN’. Proc. 2012 Int. Symp. on Instrumentation and Measurement, Sensor Network and Automation (IMSNA), 2012, vol. 2, no. 1, pp. 374–376.
-
-
53)
-
49. Irani, R., Nasrollahi, K., Simon, M.O., et al: ‘Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, 2015, pp. 88–96.
-
-
54)
-
18. Li, J., Poulton, G., Guo, Y., et al: ‘Face recognition based on multiple region features’. Proc. VIIth Digital Image Computing: Techniques and Applications, 2003, pp. 69–78.
-
-
1)