Titulo:
Inteligencia computacional para la medición de presencia de dolor mediante el uso de señales electrofisiológicas
.
Sumario:
El dolor es un problema de salud que afecta a las personas física y emocionalmente.Para determinar el nivel de dolor experimentado, se realiza una encuesta que implicaautoevaluación por parte del paciente y capacidades de comunicación verbal o facial. En esteartículo, se presenta la comparación de los resultados de dos algoritmos computacionalespara dos tipos de clasificación: el primero discrimina entre dolor y no dolor, el segundoclasifica tres niveles de dolor. Los algoritmos empleados fueron Máquina de SoporteVectorial (SVM) y el método de Análisis de Discriminante Cuadrático (QDA). Se indujodolor agudo a 15 participantes por electroestimulación, se evaluó electromiografía (EMG),electrocardiografía (ECG), actividad electrodérmica (EDA),... Ver más
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Revista EIA - 2023
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Inteligencia computacional para la medición de presencia de dolor mediante el uso de señales electrofisiológicas Computational Intelligence to Assess the Existence of Pain, Based on the Use of Electrophysiological Signals El dolor es un problema de salud que afecta a las personas física y emocionalmente.Para determinar el nivel de dolor experimentado, se realiza una encuesta que implicaautoevaluación por parte del paciente y capacidades de comunicación verbal o facial. En esteartículo, se presenta la comparación de los resultados de dos algoritmos computacionalespara dos tipos de clasificación: el primero discrimina entre dolor y no dolor, el segundoclasifica tres niveles de dolor. Los algoritmos empleados fueron Máquina de SoporteVectorial (SVM) y el método de Análisis de Discriminante Cuadrático (QDA). Se indujodolor agudo a 15 participantes por electroestimulación, se evaluó electromiografía (EMG),electrocardiografía (ECG), actividad electrodérmica (EDA), y electroencefalografía (EEG), yse le pidió a los participantes reportar el dolor percibido mediante la escala análoga visual.Posteriormente se adquirieron características de las señales asociadas al dolor. Se realizarontres análisis: clasificación binaria con múltiples variables, binaria con una característica yclasificación de tres niveles con varias características. Se compararon los algoritmos SVM yQDA utilizando la matriz de confusión y el costo computacional. Para la clasificación binariala exactitud del SVM fue del 88,02% y del QDA del 70,78%, con un costo computacional de9,587s y 3,023s respectivamente. Pain is a health problem that affects people physically and emotionally. To determine thepain experimented, a survey is carried out, which implies self-evaluation, honesty, andverbal or facial communication capability. In this paper, we present a comparison of twocomputational algorithms for two classifiers: the first classifier discriminates betweenpain and no pain, and the second one classifies three levels of pain. The algorithmsemployed were the support vector machine (SVM) and a quadratic discriminant analysismethod (QDA). Acute pain was induced in fifteen participants by electrostimulation,during the experiment we assessed electromyography (EMG), electrocardiography (ECG),electrodermal activity (EDA), and electroencephalography (EEG), as well we asked theparticipants to report their pain perception using the visual analog scale. Subsequently, weextracted features related to pain assessment from the acquired signals. Three analyseswere performed, binary classifications with multiple features, binary classifications withone feature, and three-level classifications with various features. We compared the SVM andthe QDA algorithms using the confusion matrix, and the computational cost. For the binaryclassification, the SVM algorithm accuracy was 88.02% and the QDA algorithm accuracy was70.78%, with a computational cost of 9.587 s and 3.023 s, respectively. Peñuela, Lina María Porras Hilarión, Edinson Felipe Electrophysiological signals ; Pain assessment Feature extraction Support vector machine Quadratic discriminant analysis Señales electrofisiológicas Medición de dolor Extracción de características Máquina de soporte vectorial Análisis de Discriminante Cuadrático 20 40 Núm. 40 , Año 2023 : Tabla de contenido Revista EIA No. 40 Artículo de revista Journal article 2023-12-19 00:00:00 2023-12-19 00:00:00 2023-12-19 application/pdf Fondo Editorial EIA - Universidad EIA Revista EIA 1794-1237 2463-0950 https://revistas.eia.edu.co/index.php/reveia/article/view/1683 10.24050/reia.v20i40.1683 https://doi.org/10.24050/reia.v20i40.1683 eng https://creativecommons.org/licenses/by-nc-nd/4.0 Revista EIA - 2023 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0. 4011 pp. 1 24 Bellmann P.; Schwenker F. (2020). Automated pain assessment: Is it useful to combine person-specific data samples?. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Caberra, ACT, Australia. pp. 1588–1593. DOI: 10.1109/SSCI47803.2020.9308279 Breau L. (2010). The science of pain measurement and the frustration of clinical pain assessment: Does an individualized numerical rating scale bridge the gap for children with intellectual disabilities? PAIN. 150(2), pp. 213-214. DOI: 10.1016/j.pain.2010.03.029 Briggs M.; Closs J. S. (1999). A descriptive study of the use of visual analogue scales and verbal rating scales for the assessment of postoperative pain in orthopedic patients. Journal of Pain and Symptom Management. 18(6), pp. 438–446. DOI: 10.1016/s0885-3924(99)00092-5. Díaz, R.; Marulanda, F. (2019). Dolor crónico nociceptivo y neuropático en población adulta de Manizales (Colombia). Acta Médica Colombiana, 36(1), pp. 10-17. DOI: 10.36104/amc.2011.151 Christie S.; di Fronso S.; Bertollo M.; Werthner P. (2017). Individual alpha peak frequency in ice hockey shooting performance. Frontiers in Psychology. 8, p. 762. DOI: 10.3389/fpsyg.2017.00762 Egede, J. O.; Song, S.; Olugbade, T. A.; Wang, C.; Williams, A. C. D. C.; Meng, H.; Aung, M.; Lane, N. D.; Valstar, M.; Bianchi-Berthouze, N. (2020). EMOPAIN challenge 2020: Multimodal pain evaluation from facial and bodily expressions. 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG), Buenos Aires, Argentina. pp. 849–856. DOI: 10.1109/FG47880.2020.00078 Erdogan, B.; Ogul, H. (2020). Objective pain assessment using vital signs. Procedia Computer Science. 170, pp. 947–952. DOI:10.1016/j.procs.2020.03.103 Hadjileontiadis, L. J. (2015). Eeg-based tonic cold pain characterization using wavelet higher order spectral features. IEEE Transactions on Biomedical Engineering. 62(8), pp. 1981–1991. DOI: 10.1109/TBME.2015.2409133 Hadjileontiadis, L. J. (2018). Continuous wavelet transform and higher-order spectrum: combinatory potentialities in breath sound analysis and electroencephalogram-based pain characterization. Philosophical Transactions of The Royal Society a Mathematical, physical, and engineering sciences. 376 (2126). DOI: 10.1098/rsta.2017.0249 Hassan, T.; Seuß, D.; Wollenberg, J.; Weitz, K.; Kunz, M.; Lautenbacher, S.; Garbas, J. U.; Schmid, U. (2021). Automatic detection of pain from facial expressions: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(6), pp. 1815–1831. DOI: 10.5121/ijcses.2012.3604. 47 Hautala, A. J.; Karppinen, J.; Sepp ̈anen, T. (2016). Short-term assessment of autonomic nervous system as a potential tool to quantify pain experience. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando,FL, USA. pp. 2684–2687. DOI: 10.1109/EMBC.2016.7591283 Hung, C.; Shen, T.; Liang, C.; Wu, W. (2014). Using surface electromyography (semg) to classify low back pain based on lifting capacity evaluation with principal component analysis neural network method. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Chicago, IL, USA. pp. 18–21. DOI: 10.1109/EMBC.2014.6943518 Jollant, F.; Voegeli, G.; Kordsmeier, N. C.; Carbajal, J. M.; Richard-Devantoy, S.; Turecki, G.; Caceda, R. (2019). A visual analog scale to measure psychological and physical pain: A preliminary validation of the ppp-vas in two independent samples of depressed patients. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 90, pp.55–61. DOI: 10.1016/j.pnpbp.2018.10.018 Kostyunina, M. B.; Kulikov, M. A. (1996). Frequency characteristics of eeg spectra in the emotions. Neuroscience and Behavioral Physiology. 26(4), pp. 340–343. DOI: 10.1007/BF02359037 Lusher, J.; Elander, J.; Bevan, D.; Telfe,r P.; Burton, B. (2006). Analgesic addiction and pseudo-addiction in painful chronic illness. The Clinical Journal of Pain. 22(3). DOI: 10.1097/01.ajp.0000176360.94644.41 Medrano, R.; Varela, A.; Domínguez, M.; PardM, G.; Acosta, Y.; Pardo, G. (2010). Aspectos epidemiológicos relacionados con el dolor en la población adulta. Revista Archivo Médico de Camagüey. 14(4). http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1025-02552010000400013&lng=es&tlng=es. Monroe, T. B.; Misra, S.; Habermann, R. C.; Dietrich, M. S.; Bruehl, S. P.; Cowan, R. L.; Newhouse, P. A.; Simmons, S. F. (2015). Specific physician orders improve pain detection and pain reports in nursing home residents: Preliminary data. Pain management nursing: official journal of the American Society of Pain Management Nurses. 16(5), pp. 770–780. DOI: 10.1016/j.pmn.2015.06.002 Nir, R. R.; Sinai, A.; Raz, E.; Sprecher, E.; Yarnitsky, D. (2010). Pain assessment by continuous eeg: Association between subjective perception of tonic pain and peak frequency of alpha oscillations during stimulation and at rest. Brain research. 1344, pp. 77–86. DOI: 10.1016/j.brainres.2010.05.004 Nisbet, G.; Sehgal, A. (2019). Pharmacology in the management of chronic pain. Anaesthesia and Intensive Care Medicine. 20(10), pp. 555 – 558. DOI:10.1016/j.mpaic.2019.07.009 Nora D. (2014). America’s addiction to opioids: Heroin and prescription drug abuse. Pearson Educacion. Padmanabhan S, SindhuG. 2014. Design of an ecg acquisition device for the nonlinear analysis of heart rate variability (hrv). 02 Petrovic, P.; Petersson, K. M.; Ghatan, P.; Stone-Elander, S.; Ingvar, M. (2000). Pain-related cerebral activation is altered by a distracting cognitive task. Pain. 85, pp. 19–30. DOI: 10.1016/s0304-3959(99)00232-8 Pikulkaew, K.; Chouvatut, V. (2021). Enhanced pain detection and movement of motion with data augmentation based on deep learning. 2021 13th International Conference on Knowledge and Smart Technology (KST), Bangsaen, Chounburi, Thailand. pp. 197–201. DOI: 10.1109/KST51265.2021.9415827 Pouromran, F.; Radhakrishnan, S.; Kamarthi S. (2021). Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. PLoS One. 16(7). DOI: 10.1371/journal.pone.0254108 Lo Presti, L.; La Cascia, M. (2017). Boosting hankel matrices for face emotion recognition and pain detection. Computer Vision and Image Understanding. 156, pp.19–33. DOI: 10.1016/J.CVIU.2016.10.007 Rathee, N.; Ganotra, D. (2015). A novel approach for pain intensity detection based on facial feature deformations. Journal of Visual Communication and Image Representation. 33, pp. 247 -254. DOI: 10.1016/J.JVCIR.2015.09.007 Rodriguez, P.; Cucurull, G.; González, J.; Gonfaus, J. M.; Nasrollahi, K.; Moeslund, T. B.; Roca, F. X. (2022). Deep pain: Exploiting long short-term memory networks for facial expression classification. IEEE Transactions on Cybernetics. 52(5), pp. 3314-3324. DOI: 10.1109/TCYB.2017.2662199 Rojo, R.; Prados-Frutos, J. C.; López-Valverde, A. (2015). Pain assessment using the facial action coding system. A systematic review. Medicina Clínica (English Edition). 145(8), pp. 350–355. DOI: 10.1016/j.medcli.2014.08.010 Roy, S. D.; Bhowmik, M. K.; Saha, P.; Ghosh, A. K. (2016). An approach for automatic pain detection through facial expression. Procedia Computer Science. 84, pp. 99–106. DOI:10.1016/j.procs.2016.04.072 Rupenga, M.; Vadapalli, H. B. (2016). Automatic spontaneous pain recognition using supervised classification learning algorithms. 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), South Africa, Stellenbosch. IEEE. pp. 1-6. DOI:10.1109/ROBOMECH.2016.7813150 Siqueira, S. R. D. T.; de Siqueira, J. T. T. T.; Teixeira, M. J. (2020). Chronic pain, somatic unexplained complaints and multimorbidity: A mutimorbidity painful syndrome?. Medical Hypotheses. 138, p. 109598. DOI: 10.1016/j.mehy.2020.109598. Stahlschmidt, L.; Friedrich, Y.; Zernikow, B.; Wager, J. (2018). Assessment of pain-related disability in pediatric chronic pain: A comparison of the functional disability inventory and the pediatric pain disability index. Clinical Journal of Pain. 34 (2), pp. 1173-1179. DOI: 10.1097/AJP.0000000000000646 Subramaniam, S. D.; Dass, B. (2021). Automated nociceptive pain assessment using physiological signals and a hybrid deep learning network. IEEE Sensors Journal. 21(3), pp. 3335–3343. DOI: 10.1109/JSEN.2020.3023656. Susam, B.; Akcakaya, M.; Nezamfar, H.; Diaz, D.; Xu, X.; de Sa, V.; Craig, K.; Huang, J.; Goodwin, M. (2018). Automated pain assessment using electrodermal activity data and machine learning. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA. IEEE Sensors Journal, pp. 372–375. DOI: 10.1109/JSEN.2020.3023656. Susam, B. T.; Riek, N. T.; Akcakaya, M.; Xu, X.; de Sa,, V. R.; Nezamfar, H.; Diaz, D.; Craig, K. D.; Good-win, M. S.; Huang, J. S. (2022). Automated pain assessment in children using electrodermal activity and video data fusion via machine learning. IEEE Transactions on Biomedical Engineering. 69(1), pp. 422–431. DOI: 10.1109/TBME.2021.3096137 Thiam, P.; Hihn, H.; Braun, D.A.; Kestler, H.A.; Schwenker, F. (2021). Multi-modal pain intensity assessment based on physiological signals: A deep learning perspective. Frontiers in Physiology. 12. https://doi.org/10.3389/fphys.2021.720464 Van, A. J.; Van den, W. (2015). The misuse of prescription opioids: A threat for Europe? Current Drug Abuse Reviews, 8(1), pp. 3–14. DOI: 10.2174/187447370801150611184218 Wang, R.; Xu, K.; Feng, H.; Chen, W. (2020). Hybrid RNN-ANN based deep physiological network for pain recognition. 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), Montreal, QC, Canada. Institute of Electrical and Electronics Engineers (IEEE), pp. 5584–5587. DOI: 10.1109/EMBC44109.2020.9175247. Wong, T.T.; Yeh, P.Y. (2020). Reliable accuracy estimates from fold cross validation. IEEE Transactions on Knowledge and Data Engineering. 32(8):1586–1594. DOI: 10.1109/TKDE.2019.2912815 Yang, F.; Banerjee, T.; Panaggio, M. J.; Abrams, D. M.; Shah, N.R. (2019). Continuous pain assessment using ensemble feature selection from wearable sensor data. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, Institute of Electrical and Electronics Engineers (IEEE), pp. 569–576. DOI: 10.1109/BIBM47256.2019.8983282 https://revistas.eia.edu.co/index.php/reveia/article/download/1683/1564 info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 http://purl.org/redcol/resource_type/ART info:eu-repo/semantics/publishedVersion http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text Publication |
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UNIVERSIDAD EIA |
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https://nuevo.metarevistas.org/UNIVERSIDADEIA/logo.png |
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Colombia |
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Revista EIA |
| title |
Inteligencia computacional para la medición de presencia de dolor mediante el uso de señales electrofisiológicas |
| spellingShingle |
Inteligencia computacional para la medición de presencia de dolor mediante el uso de señales electrofisiológicas Peñuela, Lina María Porras Hilarión, Edinson Felipe Electrophysiological signals ; Pain assessment Feature extraction Support vector machine Quadratic discriminant analysis Señales electrofisiológicas Medición de dolor Extracción de características Máquina de soporte vectorial Análisis de Discriminante Cuadrático |
| title_short |
Inteligencia computacional para la medición de presencia de dolor mediante el uso de señales electrofisiológicas |
| title_full |
Inteligencia computacional para la medición de presencia de dolor mediante el uso de señales electrofisiológicas |
| title_fullStr |
Inteligencia computacional para la medición de presencia de dolor mediante el uso de señales electrofisiológicas |
| title_full_unstemmed |
Inteligencia computacional para la medición de presencia de dolor mediante el uso de señales electrofisiológicas |
| title_sort |
inteligencia computacional para la medición de presencia de dolor mediante el uso de señales electrofisiológicas |
| title_eng |
Computational Intelligence to Assess the Existence of Pain, Based on the Use of Electrophysiological Signals |
| description |
El dolor es un problema de salud que afecta a las personas física y emocionalmente.Para determinar el nivel de dolor experimentado, se realiza una encuesta que implicaautoevaluación por parte del paciente y capacidades de comunicación verbal o facial. En esteartículo, se presenta la comparación de los resultados de dos algoritmos computacionalespara dos tipos de clasificación: el primero discrimina entre dolor y no dolor, el segundoclasifica tres niveles de dolor. Los algoritmos empleados fueron Máquina de SoporteVectorial (SVM) y el método de Análisis de Discriminante Cuadrático (QDA). Se indujodolor agudo a 15 participantes por electroestimulación, se evaluó electromiografía (EMG),electrocardiografía (ECG), actividad electrodérmica (EDA), y electroencefalografía (EEG), yse le pidió a los participantes reportar el dolor percibido mediante la escala análoga visual.Posteriormente se adquirieron características de las señales asociadas al dolor. Se realizarontres análisis: clasificación binaria con múltiples variables, binaria con una característica yclasificación de tres niveles con varias características. Se compararon los algoritmos SVM yQDA utilizando la matriz de confusión y el costo computacional. Para la clasificación binariala exactitud del SVM fue del 88,02% y del QDA del 70,78%, con un costo computacional de9,587s y 3,023s respectivamente.
|
| description_eng |
Pain is a health problem that affects people physically and emotionally. To determine thepain experimented, a survey is carried out, which implies self-evaluation, honesty, andverbal or facial communication capability. In this paper, we present a comparison of twocomputational algorithms for two classifiers: the first classifier discriminates betweenpain and no pain, and the second one classifies three levels of pain. The algorithmsemployed were the support vector machine (SVM) and a quadratic discriminant analysismethod (QDA). Acute pain was induced in fifteen participants by electrostimulation,during the experiment we assessed electromyography (EMG), electrocardiography (ECG),electrodermal activity (EDA), and electroencephalography (EEG), as well we asked theparticipants to report their pain perception using the visual analog scale. Subsequently, weextracted features related to pain assessment from the acquired signals. Three analyseswere performed, binary classifications with multiple features, binary classifications withone feature, and three-level classifications with various features. We compared the SVM andthe QDA algorithms using the confusion matrix, and the computational cost. For the binaryclassification, the SVM algorithm accuracy was 88.02% and the QDA algorithm accuracy was70.78%, with a computational cost of 9.587 s and 3.023 s, respectively.
|
| author |
Peñuela, Lina María Porras Hilarión, Edinson Felipe |
| author_facet |
Peñuela, Lina María Porras Hilarión, Edinson Felipe |
| topic |
Electrophysiological signals ; Pain assessment Feature extraction Support vector machine Quadratic discriminant analysis Señales electrofisiológicas Medición de dolor Extracción de características Máquina de soporte vectorial Análisis de Discriminante Cuadrático |
| topic_facet |
Electrophysiological signals ; Pain assessment Feature extraction Support vector machine Quadratic discriminant analysis Señales electrofisiológicas Medición de dolor Extracción de características Máquina de soporte vectorial Análisis de Discriminante Cuadrático |
| topicspa_str_mv |
Señales electrofisiológicas Medición de dolor Extracción de características Máquina de soporte vectorial Análisis de Discriminante Cuadrático |
| citationvolume |
20 |
| citationissue |
40 |
| citationedition |
Núm. 40 , Año 2023 : Tabla de contenido Revista EIA No. 40 |
| publisher |
Fondo Editorial EIA - Universidad EIA |
| ispartofjournal |
Revista EIA |
| source |
https://revistas.eia.edu.co/index.php/reveia/article/view/1683 |
| language |
eng |
| format |
Article |
| rights |
https://creativecommons.org/licenses/by-nc-nd/4.0 Revista EIA - 2023 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0. info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| references_eng |
Bellmann P.; Schwenker F. (2020). Automated pain assessment: Is it useful to combine person-specific data samples?. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Caberra, ACT, Australia. pp. 1588–1593. DOI: 10.1109/SSCI47803.2020.9308279 Breau L. (2010). The science of pain measurement and the frustration of clinical pain assessment: Does an individualized numerical rating scale bridge the gap for children with intellectual disabilities? PAIN. 150(2), pp. 213-214. DOI: 10.1016/j.pain.2010.03.029 Briggs M.; Closs J. S. (1999). A descriptive study of the use of visual analogue scales and verbal rating scales for the assessment of postoperative pain in orthopedic patients. Journal of Pain and Symptom Management. 18(6), pp. 438–446. DOI: 10.1016/s0885-3924(99)00092-5. Díaz, R.; Marulanda, F. (2019). Dolor crónico nociceptivo y neuropático en población adulta de Manizales (Colombia). Acta Médica Colombiana, 36(1), pp. 10-17. DOI: 10.36104/amc.2011.151 Christie S.; di Fronso S.; Bertollo M.; Werthner P. (2017). Individual alpha peak frequency in ice hockey shooting performance. Frontiers in Psychology. 8, p. 762. DOI: 10.3389/fpsyg.2017.00762 Egede, J. O.; Song, S.; Olugbade, T. A.; Wang, C.; Williams, A. C. D. C.; Meng, H.; Aung, M.; Lane, N. D.; Valstar, M.; Bianchi-Berthouze, N. (2020). EMOPAIN challenge 2020: Multimodal pain evaluation from facial and bodily expressions. 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG), Buenos Aires, Argentina. pp. 849–856. DOI: 10.1109/FG47880.2020.00078 Erdogan, B.; Ogul, H. (2020). Objective pain assessment using vital signs. Procedia Computer Science. 170, pp. 947–952. DOI:10.1016/j.procs.2020.03.103 Hadjileontiadis, L. J. (2015). Eeg-based tonic cold pain characterization using wavelet higher order spectral features. IEEE Transactions on Biomedical Engineering. 62(8), pp. 1981–1991. DOI: 10.1109/TBME.2015.2409133 Hadjileontiadis, L. J. (2018). Continuous wavelet transform and higher-order spectrum: combinatory potentialities in breath sound analysis and electroencephalogram-based pain characterization. Philosophical Transactions of The Royal Society a Mathematical, physical, and engineering sciences. 376 (2126). DOI: 10.1098/rsta.2017.0249 Hassan, T.; Seuß, D.; Wollenberg, J.; Weitz, K.; Kunz, M.; Lautenbacher, S.; Garbas, J. U.; Schmid, U. (2021). Automatic detection of pain from facial expressions: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(6), pp. 1815–1831. DOI: 10.5121/ijcses.2012.3604. 47 Hautala, A. J.; Karppinen, J.; Sepp ̈anen, T. (2016). Short-term assessment of autonomic nervous system as a potential tool to quantify pain experience. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando,FL, USA. pp. 2684–2687. DOI: 10.1109/EMBC.2016.7591283 Hung, C.; Shen, T.; Liang, C.; Wu, W. (2014). Using surface electromyography (semg) to classify low back pain based on lifting capacity evaluation with principal component analysis neural network method. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Chicago, IL, USA. pp. 18–21. DOI: 10.1109/EMBC.2014.6943518 Jollant, F.; Voegeli, G.; Kordsmeier, N. C.; Carbajal, J. M.; Richard-Devantoy, S.; Turecki, G.; Caceda, R. (2019). A visual analog scale to measure psychological and physical pain: A preliminary validation of the ppp-vas in two independent samples of depressed patients. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 90, pp.55–61. DOI: 10.1016/j.pnpbp.2018.10.018 Kostyunina, M. B.; Kulikov, M. A. (1996). Frequency characteristics of eeg spectra in the emotions. Neuroscience and Behavioral Physiology. 26(4), pp. 340–343. 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