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Pain detection evaluated from electroencephalographic signals
Detección de dolor apartir de señales de EEG
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Pain detection evaluated from electroencephalographic signals. (2022). Revista EIA, 19(38), 3829 pp. 1-18. https://doi.org/10.24050/reia.v19i38.1577
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The evaluation of pain allows the detection of medical conditions and defines the procedure to treat them. Medical staff measures pain by patient´s self-report. Nevertheless, in some cases, it is difficult or impossible for the patient to communicate the level of pain perceived. In these cases, it is useful to evaluate pain employing different techniques. In this paper, we propose the evaluation of pain through a procedure based on the analysis of the electroencephalographic signals. The algorithms were evaluated in an experiment with 14 participants where the pain was induced with an electrodiagnostic system. The participants were males and females between 18 and 33 years old. To classify between pain and no pain, we employed neural networks with an accuracy of 74,19 %.
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- Grabowski, S. R., & Tortora, G. J. (2000). Principles of anatomy and physiology. New York/Chichester: Wiley.
- Tortora, G. J., & Nielsen, M. T. (1995). Human Anatomy. Harper Collins College Pub.
- Ilana, E. (1979). Pain terms; a list with definitions and notes on usage. Pain, 6, 249.
- Manworren, R. C., & Stinson, J. (2016). Pediatric pain measurement, assessment, and evaluation. In Seminars in pediatric neurology (Vol. 23, No. 3, pp. 189-200). WB Saunders. DOI: https: / / doi .org /10.1016/j.spen.2016.10.001.
- Kagita, J., & Mitsukura, Y. (2018). Quantification of pain degree by frequency features of single-chanelled EEG. In 2018 IEEE 15th International Workshop on Advanced Motion Control (AMC) (pp. 359-363). IEEE.
- Tatum IV, W. O. (2021). Handbook of EEG interpretation. Springer Publishing Company.
- Bonotis, P. A., Tsouros, D. C., Smyrlis, P. N., Tzallas, A. T., Giannakeas, N., Glavas, E., & Tsipouras, M. G. (2019). Automated Assessment of Pain Intensity based on EEG Signal Analysis. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 583-588). IEEE.
- Panavaranan, P., & Wongsawat, Y. (2013). EEG-based pain estimation via fuzzy logic and polynomial kernel support vector machine. In The 6th 2013 Biomedical Engineering International Conference (pp. 1-4). IEEE.
- 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, 77-86.
- Cao, T., Wang, Q., Liu, D., Sun, J., & Bai, O. (2020). Resting state EEG-based sudden pain recognition method and experimental study. Biomedical Signal Processing and Control, 59, 101925.
- Tatum IV, W. O. (2021). Handbook of EEG interpretation. Springer Publishing Company.
- Bai, Y., Hu, Y., & Zhang, Z. (2016). Spontaneous EEG-based normalization of pain-evoked neural responses: Effect on improving the accuracy of pain prediction. In 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (pp. 1-4). IEEE.
- Yu, M., Sun, Y., Zhu, B., Zhu, L., Lin, Y., Tang, X., ... & Dong, M. (2020). Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG. Neurocomputing, 378, 270-282.
