ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
María J. Toscano Nuño
Universidad de Guadalajara
Juan Hugo Sanchez Avila
Universidad de Guadalajara
0000-0002-6727-0166
Sergio Alfonso Sánchez Manzo
Universidad de Guadalajara
0000-0003-0548-2232
Aurora Espinoza Valdéz
Universidad de Guadalajara
0000-0002-7185-077X
Ricardo A. Salido Ruíz
Universidad de Guadalajara
0000-0001-6135-9306
Acerca de
Peripheral Neuropathy are lesions of the peripheral nervous system, they could be caused by a wide variety of problems being the most common worldwide, diabetes mellitus. The most common methods of detection are a clinical diagnosis and an electromyography with invasive electrodes. Both of them represent a risk to the patient because they can cause lesions or infections, for these reasons, the objective of this work is to propose a non-invasive method, based on surface electromyography and that, supported by statistical analysis, may suggest the presence of Peripheral Neuropathies. Based on the literature, two muscles (flexor digits brevis and abductor digiti minimi) responsible for the mobility of the cortexes were identified as suitable for the study.
Once the information was acquired, it was conditioned though digital filters, rectification and smoothing of the signal and later processed to extract the characteristics (in time and in frequency domain) of the signal which were the input to different classifiers in order to determine their performance in differentiating subjects with neuropathy from healthy subjects. The results indicate that it is possible to differentiate both groups with a percentage greater than 83% with at least 5 of the classification algorithms tested in this work: Weighted KNN (84%), RUSBoosted Trees (83.3%), Quadratic SVM (83.3%), Cubic KNN (83.3%) y Medium Gaussian SVM (83.3%). However, it is necessary to increase the sample size to corrob- orate the good performance of this methodology.
Referencias
National Institute of Neurological Disorders and Stroke, “Neuropatía periférica” Bethesda, USA, 2016. [On line]. Available in: https://espanol.ninds.nih.gov/trastornos/neuropa- tia_periferica.htm.
International Diabetes Federation. “DF Diabetes Atlas”, 9a ed. Bruselas, Bélgica: 2019. [On line]. Available in: http://www.diabetesatlas.org
Vásquez, C.: “iMedPub Journals Detección de Neuropatía Diabética Periférica en Adultos Mayores de 60 Años en el Centro de Salud ‘ México BID ’ de Colima , México Detection of Diabetic Peripheral Neuropathy in Adults over 60 Years Old at the » Mexico BID » Health Center,” Archivos De Medicina, vol. 14, no. 4, pp. 1–6, 2018.
Mayo Foundation for Medical Education and Research, “Neuropatía periférica,” USA, 2019. [On line]. Available in: https://www.mayoclinic.org/es-es/diseases-conditions/peripheral- neuropathy/diagnosis-treatment/drc-20352067.
Conamed, C. and Dubón, C.: “Pie diabético,” Revista de la Facultad de Medicina, vol. 56, no. 4, pp. 47–52, 2013.
Cisneros, N., Ascencio, I., Libreros, V., Rodríguez, H., Campos, Á., Dávila, J., Kumate, J., Borja. V.: “Índice de amputaciones de extremidades inferiores en pacientes con diabetes”, Revista Médica del Instituto Mexicano del Seguro Social, vol. 54, no. 4, pp. 471-479, 2016.
Pradilla G. et al., Neuroepidemiologia en Santander, 1st ed. pp. 131-143.
Tierney, L., Saint, S., Hooley, M., Hurtado Chong A., and Pineda Sánchez G.: Manual de diagnóstico clínico tratamiento, 4th ed. México: Mc Graw-Hill, 2011, p. 366.
Yousaf, K., Ather, H. and Saadeh, W.: (2019). Wearable Peripheral Neuropathy Detection System based on Surface Electromyography. 2019 UK/China Emerging Technologies, UCET 2019. https://doi.org/10.1109/UCET.2019.8881853
Ullah, S., and Iqbal, K.: (2020). A Preliminary Review on EMG Signals for Assessment of Diabetic Peripheral Neuropathy Disorder. 2020 7th International Conference on Electrical andElectronics Engineering, ICEEE 2020, 42–46. https://doi.org/10.1109/ICEEE49618.2020.9102488
Texas Instruments Incorporated, “INA12x Precision, Low-Power Instrumentation Amplifi- ers”, 2019. [On line]. Available in: http://www.ti.com/lit/ds/symlink/ina128.pdf.
Correa-Figueroa, J. L., Morales-Sánchez, E., Huerta-Ruelas, J. A., González-Barbosa, J. J. and Cárdenas-Pérez, C. R.: “Sistema de adquisición de señales SEMG para la detección de fatiga muscular”, Rev. Mex. Ing. Biomed., vol. 37, núm. 1, pp. 17–27, 2016.
De Luca, C. J.: “Electromyographic. Encyclopedia of Medical Devices and Instrumenta- tion”. Ed. John G. Webster. 2006. Massachusetts. Ed. John Wiley Publisher, pp 98-106.
[14] Romo Romero, H., Realpe, J. and Jojoa, P.: “Análisis de señales emg superficiales y su aplicación en control de prótesis de mano”, Av. en Sist. e Informática, vol. 4, núm. 1, pp. 127–136, 2007.
Netter, F. H.: Colección ciba de ilustraciones médicas, Tomo 8.1, Primera ed. Barcelona, España: Ediciones Científicas y Técnicas, S.A., 1990.
U.S. National Library of Medicine, “Neuropatía periférica,” Bethesda, USA, 2018. [On line]. Available in: https://medlineplus.gov/spanish/ency/article/000593.htm.
Gray, H.: Anatomía del cuerpo humano. Londres, Inglaterra, 2000. [En línea]. Available in: https://www.bartleby.com/107/
Sánchez, I., Ferrero, A., Aguilar, J. J., Climent, J. M. and Conejero, J. A.: Manual SERMEF de rehabilitación y medicina física. Madrid: Medicina Panamericana, 2016.
Phinyomark, A., Phukpattaranont, P., and Limsakul, C.: “Feature reduction and selection for EMG signal classification,” Expert Syst. Appl., vol. 39, no. 8, pp. 7420–7431, 2012, doi: 10.1016/j.eswa.2012.01.102.
Abbaspour, S., Lindén, M., Gholamhosseini, H., Naber, A. and Ortiz-Catalan, M.: “Evalu- ation of surface EMG-based recognition algorithms for decoding hand movements,” Med. Biol. Eng. Comput., vol. 58, no. 1, pp. 83–100, 2019, doi: 10.1007/s11517-019-02073-z.