Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA Analysis
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Abstract
Purpose: Pain is an unpleasant sensation that is important in all therapeutic conditions. So far, some researches have been done on pain assessment and cognition, and researchers have come to evaluate pain through different tests and methods. Since the occurrence of pain causes along with activation of a long network in brain regions, so recognition of dynamical changes of the brain in pain states is helpful for pain detection using Electroencephalogram (EEG) signal.
Materials and methods: The aim of this research is to investigate dynamical changes of the brain for pain detection using EEG at the time of happening phasic pain. For this purpose, at the first step phasic pain is produced using coldness, then dynamical features via EEG are analyzed via Recurrence Quantification Analysis (RQA) method and finally Rough neural network classifier has been used for achieving accuracy to detect and categorize pain and non-pain states.
Results: The performance of the classification procedure is 95.25 4%. That is compared with other research, it is a novel method of using rough neural network for distinguishing pain from non-pain states.
Conclusion: The simulation results proved that cerebral behaviors are detectable during pain. Also, one of the most merits of the proposed method is the high accuracy of classifier for an investigation into dynamical fatures of the brain during happening pain. Finally, pain detection can improve and upgrade medical methods.
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[2] Tatum IV, W.O., Handbook of EEG interpretation. 2014: Demos Medical Publishing.
[3] Chernecky, C.C. and B.J. Berger, Laboratory Tests and Diagnostic Procedures-E-Book. 2012: Elsevier Health Sciences.
[4] Sanei, S. and J. Chambers, Brain rhythms. EEG Signal Processing, 2007: p. 10-13.
[5] Dehghanpour, P. and Z. Einalou, Evaluating the features of the brain waves to quantify ADHD improvement by neurofeedback. Technology and Health Care, 2017. 25(5): p. 877-885.
[6] Heidari, H. and Z. Einalou, SSVEP extraction applying wavelet transform and decision tree with bays classification. International Clinical Neuroscience Journal, 2017. 4(3): p. 91-97.
[7] Eslamieh, N. and Z. Einalou, Investigation of Functional Brain Connectivity by Electroencephalogram Signals using Data Mining Technique. Journal of Cognitive Science, 2018. 19(4): p. 551-576.
[8] Freitas, R.A., Nanomedicine, volume I: basic capabilities. Vol. 1. 1999: Landes Bioscience Georgetown, TX.
[9] Bardhan, S., et al. A review on inflammatory pain detection in human body through infrared image analysis. in 2015 international symposium on advanced computing and communication (ISACC). 2015. IEEE.
[10] Colombo, B., et al., Resting-state fMRI functional connectivity: a new perspective to evaluate pain modulation in migraine? Neurological Sciences, 2015. 36(1): p. 41-45.
[11] Wey, H.-Y., et al., Simultaneous fMRI–PET of the opioidergic pain system in human brain. Neuroimage, 2014. 102: p. 275-282.
[12] Barati, Z., I. Zakeri, and K. Pourrezaei, Functional near-infrared spectroscopy study on tonic pain activation by cold pressor test. Neurophotonics, 2017. 4(1): p. 015004.
[13] Roy, S.D., et al., An approach for automatic pain detection through facial expression. Procedia Computer Science, 2016. 84: p. 99-106.
[14] Lopez-Martinez, D. and R. Picard. Multi-task neural networks for personalized pain recognition from physiological signals. in 2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). 2017. IEEE.
[15] Oshrat, Y., et al. Speech prosody as a biosignal for physical pain detection. in Conf Proc 8th Speech Prosody. 2016.
[16] Tejman-Yarden, S., et al., Heart rate analysis by sparse representation for acute pain detection. Medical & biological engineering & computing, 2016. 54(4): p. 595-606.
[17] Garcia-Larrea, L., M. Frot, and M. Valeriani, Brain generators of laser-evoked potentials: from dipoles to functional significance. Neurophysiologie clinique/Clinical neurophysiology, 2003. 33(6): p. 279-292.
[18] Lorenz, J. and L. Garcia-Larrea, Contribution of attentional and cognitive factors to laser evoked brain potentials. Neurophysiologie Clinique/Clinical Neurophysiology, 2003. 33(6): p. 293-301.
[19] Mouraux, A., J.-M. Guerit, and L. Plaghki, Non-phase locked electroencephalogram (EEG) responses to CO2 laser skin stimulations may reflect central interactions between A∂-and C-fibre afferent volleys. Clinical neurophysiology, 2003. 114(4): p. 710-722.
[20] Gross, J., et al., Gamma oscillations in human primary somatosensory cortex reflect pain perception. PLoS biology, 2007. 5(5).
[21] Vatankhah, M., V. Asadpour, and R. Fazel-Rezai, Perceptual pain classification using ANFIS adapted RBF kernel support vector machine for therapeutic usage. Applied Soft Computing, 2013. 13(5): p. 2537-2546.
[22] Vatankhah, M. and A. Toliyat, Pain level measurement using discrete wavelet transform. International Journal of Engineering and Technology, 2016. 8(5): p. 380-4.
[23] Alazrai, R., et al., EEG-based tonic cold pain recognition system using wavelet transform. Neural Computing and Applications, 2017: p. 1-14.
[24] Nezam, T., et al., A novel classification strategy to distinguish five levels of pain using the EEG signal features. IEEE Transactions on Affective Computing, 2018.
[25] Mansoor, Z., et al. Pain Prediction in humans using human brain activity data. in Companion Proceedings of the The Web Conference 2018. 2018.
[26] Panavaranan, P. and Y. Wongsawat. EEG-based pain estimation via fuzzy logic and polynomial kernel support vector machine. in The 6th 2013 Biomedical Engineering International Conference. 2013. IEEE.
[27] Misra, G., et al., Automated classification of pain perception using high-density electroencephalography data. Journal of neurophysiology, 2017. 117(2): p. 786-795.
[28] Vijayakumar, V., et al., Quantifying and characterizing tonic thermal pain across subjects from EEG data using random forest models. IEEE Transactions on Biomedical Engineering, 2017. 64(12): p. 2988-2996.
[29] González Márquez, D., Filtering guide: filtering biomedical signals Matlab. 2019.
[30] Goshvarpour, A., A. Abbasi, and A. Goshvarpour, Dynamical analysis of emotional states from electroencephalogram signals. Biomedical Engineering: Applications, Basis and Communications, 2016. 28(02): p. 1650015.
[31] Marwan, N., Cross recurrence plot toolbox for matlab. Reference Manual, Version 5.19, Release 30.2, 2016.
[32] Elamir, M., W. Alatabany, and M. Aldosoky. Intelligent emotion recognition system using recurrence quantification analysis (RQA). in 2018 35th National Radio Science Conference (NRSC). 2018. IEEE.
[33] Ding, S., et al., Rough neural networks: a review. J Comput Inf Syst, 2011. 7(7): p. 2338-2346.
[34] Nowicki, R.K., Rough Neural Network Classifier, in Rough Set–Based Classification Systems. 2019, Springer. p. 95-132.
[35] MATLAB, V., 9.2. 0 (R2017a), help, plotconfusion. The MathWorks Inc.: Natick, MA, USA, 2017.
| Files | ||
| Issue | Vol 12 No 3 (2025) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v12i3.19182 | |
| Keywords | ||
| Cold Pressor Test Electroencephalogram Phasic Pain Rough Neural Network Recurrence Quantification Analysis Electroencephalogram Dynamics Pain Assessment | ||
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Tavasoli M, Einalou Z, Akhondzadeh R. Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA Analysis. Frontiers Biomed Technol. 2025;12(3):571-586.
