پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 405 |
| تعداد مقالات | 5,426 |
| تعداد مشاهده مقاله | 5,568,396 |
| تعداد دریافت فایل اصل مقاله | 5,032,190 |
A Physiological-Inspired Classification Strategy to Classify Five Levels of Pain | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 12، دوره 52، شماره 2، اسفند 2020، صفحه 283-292 اصل مقاله (1.16 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2020.18449.5213 | ||
| نویسندگان | ||
| Somayeh Afrasiabi* 1؛ Reza Boostani2؛ Mohammad Ali Masnadi-Shirazi3 | ||
| 1CSE & IT Department ECE faculty, Shiraz University,Shiraz,Iran | ||
| 2CSE & IT Department, ECE faculty, Shiraz University, Shiraz, Iran | ||
| 3School of Electrical & Computer Engineering, Shiraz University, Shiraz, Iran | ||
| چکیده | ||
| Current research on quantitative pain measurement using the electroencephalogram (EEG) signals showed a promising result just on classifying pain from no-pain states. In this paper, we go one step further introducing pain-level dependent EEG features as well as proposing a physiologically-inspired hierarchical classifier to provide promising results for differentiating five classes of pain. In this research, forty four subjects were voluntarily enrolled, each of whom executed the Cold-Pressor Test (CPT), while their EEGs were simultaneously recorded. We filtered the EEGs through the alpha band and elicited meaningful features to reveal the behavior of signals in terms of distribution, spectrum and complexity at each pain state. To assess the susceptibility of the features in classifying one/group of classes against others, Kruscall-Walis test was applied to give a clue in order to construct the structure of our decision tree, where a Bayesian Optimized support vector machine (BSVM) was trained at each node. After arranging the tree, sequential forward selection (SFS) was applied to select a customized subset of features for each node. Our results provide 93.33% accuracy for the five classes and also generate 99.8% for pain and non-pain classes, which is statistically superior (P<0.05) to state-of-the-art methods over the same dataset. | ||
| کلیدواژه‌ها | ||
| Pain measurement؛ Physiological based classifier؛ EEG signal processing, distribution of alpha band | ||
| مراجع | ||
|
[1] A. Apkarian, M.C. Bushnell, R. Treede, J. Zubieta, Human Brain Mechanisms of Pain Perception and Regulation in Health and Disease, Eur J Pain, 9(4) (2005) 463-484. [2] M. McCaffery, C.L. Pasero, Pain ratings: the fifth vital sign, Am J Nurs, 97(2) (1997) 15-16. [3] J. Shieh, C. Dai, Y. Wen, W. Sun, A Novel Fuzzy Pain Demand Index Derived From Patient-Controlled Analgesia for Postoperative Pain, IEEE Transactions on Biomedical Engineering, 54(12) (2007) 2123-2132. [4] A.V. Apkarian, M.C. Bushnell, R.-D. Treede, J.-K. Zubieta, Human brain mechanisms of pain perception and regulation in health and disease, European Journal of Pain 9(2005) 463-484. [5] K. Broderson, K. Wiech, E. Lomakina, C. Lin, J. Buhman, U. Bingel, M. Ploner, K. Stephan, I. Tracey, Decoding the Perception of Pain from FMRI Using Multivariate Pattern Analysis, Neuroimage, 63(3) (2012) 1162-1170. [6] A. Marquand, M. Howard, M. Brammer, C. Chu, S. Coen, J. Mourao-Miranda, Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes, Neuroimage, 49(3) (2010) 2178-2189. [7] M.A. Yukel, C.A. Aasted, M.P. Petcov, D. Borsook, D.A. Boas, Specificity of hemodynamic brain responce to painful stimuli: a functional near infrared spectroscopy study, Nature, Scientific Reports, 5(9469) (2015) 1-9. [8] L.J. Hadjileontiadis, EEG Based Tonic Cold Pain Characterization Using Wavelet Higher order Spectral Features, IEEE Transactions on Biomedical Engineering, 62(8) (2015) 1981-1991. [9] G. Misra, W.E. Wang, D.B. Archer, A. Roy, S.A. Coombes, Automated classification of pain perception using high-density electroencephalography data, J Neurophysiol, 117(2) (2017) 786-795. [10] R.R. Nir, R. Lev, R. Moont, Y. Granovsky, E. Sprecher, Neurophysiology of the Cortical Pain Network: Revisiting the Role of S1 in Subjective Pain Perception Via Standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA), The Journal of Pain, 9(11) (2008) 1058-1069. [11] F. Razavipour, R. Boostani, S. Kouchaki, S. Afrasiabi, Comparative Application of Non-negative Decomposition Methods in Classifying Fatigue and Non-fatigue States, Arabian Journal for Science and Engineering, 39(10) (2014) 7049-7058. [12] M. Fabri, G. Polonara, A. Quattrini, U. Salvolini, Mechanical Noxious Stimuli Cause Bilateral Activation of Parietal Operculum in Callosotomized Subjects, Cereb. Cortex, 12(4) (2002) 446-451. [13] T. Nezam, R. Boostani, V. Abootalebi, K. Rastegar, A Novel Classification Strategy to Distinguish Five Levels of Pain Using the EEG Signal Features, IEEE Transactions on Affective Computing, (2018) 1-9. [14] M. Huber, J. Bartling, D. Pachur, S. Woikowski, S. Lautenbacher, EEG Responce to Tonic Heat Pain, Exp. Brain Res. , 173(1) (2006) 14-24. [15] C. Huishi Zhang, A. Sohrabpour, Y. Lu, B. He, Spectral and spatial changes of brain rhythmic activity in response to the sustained thermal pain stimulation, Human brain mapping, 37(8) (2016) 2976-2991. [16] G. Huang, P. Xiao, Y. Huang, G. Iannetti, Z. Zhang, L. Hu, A Novel Approach to Predict Subjective Pain Perception from Single-trial Laser-evoked Potentials, Neuroimage, 1(81) (2013) 283-293. [17] S. Afrasiabi, R. Boostani, S. Koochaki, F. Zand, Presenting an effective EEG-based index to monitor the depth of anesthesia, in: The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), 2012, pp. 557-562. [18] M. Vatankhah, V. Asadpour, R. Fazelrezai, Perceptual Pain Classification Using ANFIS Adapted RBF Kernel Support Vector Machine for Terapeutic Usage, Applied Soft Computing, 13(1) (2013) 2537-2546. [19] M. Vatankhah, A. Toliyat, Pain Level Measurment Using Descrete Wavelet Transform, IJET, 8(5) (2016) 380-385. [20] E. Schulz, E.S. May, M. Postorino, L. Tiemann, M.M. Nickel, V. Witkovsky, P. Schmidt, J. Gross, M. Ploner, Prefrontal Gamma Oscillations Encode Tonic Pain in Humans, Cereb Cortex, 25(11) (2015) 4407-4414. [21] G. Garra, A.J. Singer, A. Domingo, H.C. Thode, Jr., The Wong-Baker pain FACES scale measures pain, not fear, Pediatr Emerg Care, 29(1) (2013) 17-20. [22] F. AliMardani, R. Boostani, B. Blankertz, Presenting a Spatial-Geometric EEG Feature to Classify BMD and Schizophrenic Patients, 2016, 5(2) (2016) 7. [23] A. Delorme, S. Makeig, EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis, J Neurosci Methods, 134(1) (2004) 9-21. [24] R. Elul, Gaussian behavior of the electroencephalogram: changes during performance of mental task, Science, 164(3877) (1969) 328-331. [25] S. Afrasiabi, R. Boostani, F. Zand, F. Razavipour, INTRODUCING A NOVEL INDEX FOR MEASURING DEPTH OF ANESTHESIA BASED ON VISUAL EVOKED POTENTIAL (VEP) FEATURES, Iranian Journal of Science and Technology Transactions of Electrical Engineering, 36(2) (2012) 131-146. [26] H.O. Hartley, H.A. David, Universal Bounds for Mean Range and Extreme Observation, The annals of Mathematical Statistics, 25(1) (1954) 85-99. [27] A.D. Nazhvani, R. Boostani, S. Afrasiabi, K. Sadatnezhad, Classification of ADHD and BMD patients using visual evoked potential, Clinical Neurology and Neurosurgery, 115(11) (2013) 2329-2335. [28] S. Afrasiabi, R. Boostani, M.-A. Masnadi-Shirazi, A Physiological-Inspired Classification Strategy to Classify Five Levels of Pain in: ICBME2019, Tehran, 2019. [29] G. Huang, P. Xiao, Y. Hung, G. Iannetti, Z. Zhang, L. Hu, A Novel Approach to Predict Subjective Pain Perception from Single-Trial Laser-evoked Pottential, Neuroimage, 1(81) (2013) 283-293. [30] G. Misra, W. Wang, D. Archer, A. Roy, S. Coombes, Automated Classification of Pain Perception using High density Electroencephalography Data, Neurophysiology, 117 (2017) 786-795. [31] A. Marquand, M. Hovard, M. Brammer, C. Chu, S. Coen, J. Mourao-Miranda, Quantitative Prediction of Subjective Pain Intensity from whole brain FMRI Data Using Gaussian Processes, Neuroimage, 49(3) (2010) 2178-2189. [32] J. Bergstra, Y. Bengio, Random Search for Hyper-parameter Optimization, J. Mach. Learn. Res., 13(1) (2012) 281-305. | ||
|
آمار تعداد مشاهده مقاله: 282 تعداد دریافت فایل اصل مقاله: 219 |
||