Automated assessment of pain intensity based on EEG signal analysis

Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019

Volumn , Issue , 2019, Pages 583-588

  Bonotis, Panagiotis A ^a   Tsouros, Dimosthenis C ^a   Smyrlis, Panagiotis N ^a   Tzallas, Alexandros T ^b   Giannakeas, Nikolaos ^b   Glavas, Evripidis ^b   Tsipouras, Markos G ^a,c  

^a UNIVERSITY OF WESTERN MACEDONIA   (Greece)

^b UNIVERSITY OF IOANNINA   (Greece)

^c Park of Epirus   (Greece)

Author keywords

Automated Assessment; Electroencephalogram (EEG); Pain Intensity; Portable EEG; Stochastic Forest Classifier

Indexed keywords

AUTOMATION; BIOINFORMATICS; BRAIN; COST EFFECTIVENESS; FORESTRY; SIGNAL ANALYSIS; STOCHASTIC SYSTEMS;

AUTOMATED ASSESSMENT; AUTOMATED PAIN DETECTIONS; CLASSIFICATION ACCURACY; CLINICAL CONDITIONS; ELECTRO-ENCEPHALOGRAM (EEG); FOREST CLASSIFIERS; HEALTHY VOLUNTEERS; PAIN INTENSITY;

ELECTROENCEPHALOGRAPHY;

EID: 85075765808     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBE.2019.00111     Document Type: Conference Paper

References (26)

1. J. A. Wright, "An update of systemic analgesics in children, " Anaesthesia & Intensive Care Medicine, 2019.
2. S. Walter, S. Gruss, K. Limbrecht-Ecklundt, H. C. Traue, P. Werner, A. Al-Hamadi, N. Diniz, G. M. d. Silva, and A. O. Andrade, "Automatic pain quantification using autonomic parameters, " Psychology & Neuroscience, vol. 7, no. 3, pp. 363-380, 2014.
3. J. D. Loeser and R.-D. Treede, "The kyoto protocol of iasp basic pain terminology, " Pain, vol. 137, no. 3, pp. 473-477, 2008.
4. P. M. Aslaksen, I. N. Myrbakk, R. S. Høifødt, and M. A. Flaten, "The effect of experimenter gender on autonomic and subjective responses to pain stimuli, " Pain, vol. 129, no. 3, pp. 260-268, 2007.
5. D. Madeo, E. Castellani, C. Mocenni, and E. L. Santarcangelo, "Pain perception and EEG dynamics: Does hypnotizability account for the efficacy of the suggestions of analgesia?" Physiology & behavior, vol. 145, pp. 57-63, 2015.
6. T. Nezam, R. Boostani, V. Abootalebi, and K. Rastegar, "A novel classification strategy to distinguish five levels of pain using the EEG signal features, " IEEE Transactions on Affective Computing, 2018.
7. P. Veerasarn and C. S. Stohler, "The effect of experimental muscle pain on the background electrical brain activity, " Pain, vol. 49, no. 3, pp. 349-360, 1992.
8. G. Huang, P. Xiao, Y. Hung, G. D. Iannetti, Z. Zhang, and L. Hu, "A novel approach to predict subjective pain perception from single-trial laser-evoked potentials, " Neuroimage, vol. 81, pp. 283-293, 2013.
9. C. S. Cleeland, Y. Nakamura, E. W. Howland, N. R. Morgan, K. R. Edwards, and M. Backonja, "Effects of oral morphine on cold pressor tolerance time and neuropsychological performance, " Neuropsychopharmacology, vol. 15, no. 3, pp. 252-262, 1996.
10. A. C. Chen and P. Rappelsberger, "Brain and human pain: topographic EEG amplitude and coherence mapping, " Brain topography, vol. 7, no. 2, pp. 129-140, 1994.
11. A. C. Chen, P. Rappelsberger, and O. Filz, "Topology of EEG coherence changes may reflect differential neural network activation in cold and pain perception, " Brain topography, vol. 11, no. 2, pp. 125-132, 1998.
12. M. Gram, C. Graversen, S. Olesen, and A. Drewes, "Dynamic spectral indices of the electroencephalogram provide new insights into tonic pain, " Clinical Neurophysiology, vol. 126, no. 4, pp. 763-771, 2015.
13. W. Peng, C. Babiloni, Y. Mao, and Y. Hu, "Subjective pain perception mediated by alpha rhythms, " Biological psychology, vol. 109, pp. 141-150, 2015.
14. H. Maki, T. Toda, S. Sakti, G. Neubig, and S. Nakamura, "Eeg signal enhancement using multi-channel wiener filter with a spatial correlation prior, " in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015, pp. 2639-2643.
15. F. Riaz, A. Hassan, S. Rehman, I. K. Niazi, and K. Dremstrup, "Emdbased temporal and spectral features for the classification of EEG signals using supervised learning, " IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 1, pp. 28-35, 2015.
16. E. B. Mazomenos, D. Biswas, A. Acharyya, T. Chen, K. Maharatna, J. Rosengarten, J. Morgan, and N. Curzen, "A low-complexity ecg feature extraction algorithm for mobile healthcare applications, " IEEE journal of biomedical and health informatics, vol. 17, no. 2, pp. 459-469, 2013.
17. L. J. Hadjileontiadis, "Eeg-based tonic cold pain characterization using wavelet higher order spectral features, " IEEE Transactions on Biomedical Engineering, vol. 62, no. 8, pp. 1981-1991, 2015.
18. M. Gram, C. Graversen, C. D. Mørch, T. Andresen, and A. M. Drewes, "Wavelet analysis of single-sweep pharmaco-eeg: beta-band activity correlate to the analgesic effect of buprenorphine, " in International Pharmaco-EEG Society. IPEG, 2012.
19. "Emotiv epoc headset. " [Online]. Available: http://www. emotiv. com/
20. D. C. Tsouros, M. G. Tsipouras, P. N. Smyrlis, N. Giannakeas, and A. T. Tzallas, "Random forests with stochastic induction of decision trees, " in 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2018, pp. 527-531.
21. L. Breiman, "Random forests, " Machine learning, vol. 45, no. 1, pp. 5-32, 2001.
22. J. R. Quinlan, "C4. 5: Programming for machine learning, " Morgan Kauffmann, vol. 38, 1993.
23. B. D. Ripley, "Neural networks and related methods for classification, " Journal of the Royal Statistical Society: Series B (Methodological), vol. 56, no. 3, pp. 409-437, 1994.
24. S. Haykin, Neural Networks: A Comprehensive Foundation, 1st ed. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1994.
25. G. Holmes, A. Donkin, and I. H. Witten, "Weka: A machine learning workbench, " in Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on. IEEE, 1994, pp. 357-361.
26. R. Kohavi et al., "A study of cross-validation and bootstrap for accuracy estimation and model selection, " in Ijcai, vol. 14, no. 2. Stanford, CA, 1995, pp. 1137-1145.