Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept
Background: Multiple sclerosis (MS) is a progressive and neurodegenerative disease of the central nervous system. Its symptoms vary greatly, which makes its diagnosis complex, expensive, and time‑consuming. One of its most prevalent symptoms is muscle fatigue. It occurs in about 92% of patients with...
- Autores:
-
Gomez Hernández, Marina
Olaya Mira, Natali
Viloria Barragán, Carolina
Henao Pérez, Julieta
Rojas Mora, Jessica María
Díaz Londoño, Gloria
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2023
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/51364
- Acceso en línea:
- https://hdl.handle.net/20.500.12494/51364
- Palabra clave:
- Baropodometria
Muestra de entropía
Fatiga muscular
Esclerosis multiple
Electromiografía
Baropodometry
Sample entropy
Muscle fatigue
Multiple sclerosis
Electromyography
- Rights
- openAccess
- License
- NINGUNA
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Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept |
| title |
Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept |
| spellingShingle |
Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept Baropodometria Muestra de entropía Fatiga muscular Esclerosis multiple Electromiografía Baropodometry Sample entropy Muscle fatigue Multiple sclerosis Electromyography |
| title_short |
Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept |
| title_full |
Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept |
| title_fullStr |
Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept |
| title_full_unstemmed |
Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept |
| title_sort |
Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept |
| dc.creator.fl_str_mv |
Gomez Hernández, Marina Olaya Mira, Natali Viloria Barragán, Carolina Henao Pérez, Julieta Rojas Mora, Jessica María Díaz Londoño, Gloria |
| dc.contributor.advisor.none.fl_str_mv |
Henao Pérez, Julieta |
| dc.contributor.author.none.fl_str_mv |
Gomez Hernández, Marina Olaya Mira, Natali Viloria Barragán, Carolina Henao Pérez, Julieta Rojas Mora, Jessica María Díaz Londoño, Gloria |
| dc.subject.none.fl_str_mv |
Baropodometria Muestra de entropía Fatiga muscular Esclerosis multiple Electromiografía |
| topic |
Baropodometria Muestra de entropía Fatiga muscular Esclerosis multiple Electromiografía Baropodometry Sample entropy Muscle fatigue Multiple sclerosis Electromyography |
| dc.subject.other.none.fl_str_mv |
Baropodometry Sample entropy Muscle fatigue Multiple sclerosis Electromyography |
| description |
Background: Multiple sclerosis (MS) is a progressive and neurodegenerative disease of the central nervous system. Its symptoms vary greatly, which makes its diagnosis complex, expensive, and time‑consuming. One of its most prevalent symptoms is muscle fatigue. It occurs in about 92% of patients with MS (PwMS) and is defined as a decrease in maximal strength or energy production in response to contractile activity. This article aims to compare the behavior of a healthy control (HC) with that of a patient with MS before and after muscle fatigue. Methods: For this purpose, a static baropodometric test and a dynamic electromyographic analysis are performed to calculate the area of the stabilometric ellipse, the remitting MS (RMS) value, and the sample entropy (SampEn) of the signals, as a proof of concept to explore the feasibility of this test in the muscle fatigue quantitative analysis; in addition, the statistical analysis was realized to verify the results. Results: According to the results, the ellipse area increased in the presence of muscle fatigue, indicating a decrease in postural stability. Likewise, the RMS value increased in the MS patient and decreased in the HC subject and the opposite behavior in the SampEn was observed in the presence of muscle fatigue. Conclusion: Thus, this study demonstrates that SampEn is a viable parameter to estimate muscle fatigue in PwMS and other neuromuscular diseases. |
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2023 |
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2023-06-14T14:24:58Z |
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2023-06-14T14:24:58Z |
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2023-06-09 |
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Artículo |
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DOI: 10.4103/jmss.jmss_184_21 https://hdl.handle.net/20.500.12494/51364 |
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Gómez Hernández, M., Olaya Mira, N., Viloria Barragán, C., Henao Pérez, J., Rojas Mora, J. M. y Díaz Londoño, G. (2023). Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept. [Articulo, Universidad Cooperativa de Colombia]. Repositorio Institucional Universidad Cooperativa de Colombia.https://repository.ucc.edu.co/handle/20.500.12494/51364 |
| identifier_str_mv |
DOI: 10.4103/jmss.jmss_184_21 Gómez Hernández, M., Olaya Mira, N., Viloria Barragán, C., Henao Pérez, J., Rojas Mora, J. M. y Díaz Londoño, G. (2023). Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept. [Articulo, Universidad Cooperativa de Colombia]. Repositorio Institucional Universidad Cooperativa de Colombia.https://repository.ucc.edu.co/handle/20.500.12494/51364 |
| url |
https://hdl.handle.net/20.500.12494/51364 |
| dc.relation.isversionof.none.fl_str_mv |
https://www.jmssjournal.net/text.asp?2023/13/2/153/377810 |
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Journal of Medical Signals & Sensors |
| dc.relation.references.none.fl_str_mv |
Ghasemi N, Razavi S, Nikzad E. Multiple sclerosis: Pathogenesis, symptoms, diagnoses and cell‑based therapy. Cell J 2017;19:1‑10. Goodin DS. The Epidemiology of Multiple Sclerosis: Insights to a Causal Cascade. In: Handbook of Clinical Neurology. 1st ed., Vol. 138. no. 3. Amsterdam: Elsevier B.V., 2016. Nogales‑Gaete J, Aracena R, Cepeda‑Zumaeta S, Eloiza C, Agurto P, Díaz V, et al. Clinical features of 314 patients with relapsing‑remitting multiple sclerosis. Rev Med Chil 2014;142:559‑66. Osorio‑Marcatinco V, Castro‑Suarez S, Meza‑Vega M. Cognitive characteristics of patients with relapsing ‑ remitting multiple sclerosis during relapse attended in National Institute of Neurological Sciences 2014 ‑ 2016. Rev Neuropsiquiatr 2018;81:58‑64. Oh J, Alikhani K, Bruno T, Devonshire V, Giacomini PS, Giuliani F, et al. Diagnosis and management of secondary‑progressive multiple sclerosis: Time for change. Neurodegener Dis Manag 2019;9:301‑17. Olek M, Mowry E. Pathogenesis and epidemiology of multiple sclerosis,” Up To Date; 2020. p. 1‑23. Available: https://www. uptodate.com/contents/pathogenesis‑and‑epidemiology‑of‑ multiple‑sclerosis. [Last cited on 2020 May 06]. Leray E, Moreau T, Fromont A, Edan G. Epidemiology of multiple sclerosis. Rev Neurol (Paris) 2016;172:3‑13. Negrotto L, Correale J. Evolution of multiple sclerosis prevalence and phenotype in Latin America. Mult Scler Relat Disord 2018;22:97‑102. Jiménez C, Zarco L, Castañeda C, Otálora M, Martínez A, Rosselli D. Current state of multiple sclerosis in Colombia. Acta Neurol Colomb 2015;31:385‑90. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018;17:162‑73. Diazgranados Sánchez JA, Burbano J, Herrera Escandón Á, Hidalgo JF, Gómez Betancourt LF, Chan Guevara L. Mc Donald 2010 criteria in the diagnosis of multiple sclerosis in Cali, Colombia. Acta Neurol Colomb 2013;29:247‑54. Jarmi V, De Elías R, Kiener O, Villate S, Vrech C, Barzón S. Oligoclonal bands: contribution and interpretation in patients with suspected multiple sclerosis. Acta Bioquim Clin Latinoam 2015;49:257‑65. Rojas Huerto E, Alva Diaz C, Montalvan Ayala V. Clinical changes of multiple sclerosis according to modification of the McDonald criteria. Hospital Almenara, 2001‑2015. An la Fac Med 2019;80:34‑8. Martinez‑Altarriba MC, Ramos‑Campoy O, Luna‑Calcaño IM, Arrieta‑Antón E. A review of multiple sclerosis (2). Diagnosis and treatment. Semergen 2015;41:324‑8. Castellano‑Del Castillo MA, Lacasa‑Andrade ME, Hijós‑Bitrián E, Mambrona‑Girón L, Sebastiá‑Vigatá E, Vázquez‑Sasot A. Effectiveness of rehabilitation in multiple sclerosis. Rehabilitacion 2014;48:46‑53. Yusuf F, Wijnands JM, Kingwell E, Zhu F, Evans C, Fisk JD, et al. Fatigue, sleep disorders, anaemia and pain in the multiple sclerosis prodrome. Mult Scler 2021;27:290‑302. Proessl F, Poston B, Rudroff T. Does a single application of anodal tDCS improve knee extensor fatigability in people with multiple sclerosis? Brain Stimul 2018;11:1388‑90. Newland P, Van Aman MN, Smith J, Spencer A. Relationship of fatigue to heat sensitivity in patients with multiple sclerosis: A review for management. J Nurse Pract 2018;14:100‑4. Sumowski JF, Leavitt VM. Body temperature is elevated and linked to fatigue in relapsing‑remitting multiple sclerosis, even without heat exposure. Arch Phys Med Rehabil 2014;95:1298‑302. Eken MM, Richards R, Beckerman H, van der Krogt M, Gerrits K, Rietberg M, et al. Quantifying muscle fatigue during walking in people with multiple sclerosis. Clin Biomech (Bristol, Avon) 2020;72:94‑101. Beretta‑Piccoli M, Cescon C, Barbero M, Villiger M, Clijsen R, Kool J, et al. Upper and lower limb performance fatigability in people with multiple sclerosis investigated through surface electromyography: A pilot study. Physiol Meas 2020;41:025002. Zhang X, Wang D, Yu Z, Chen X, Li S, Zhou P. EMG‑torque relation in chronic stroke: A novel EMG complexity representation with a linear electrode array. IEEE J Biomed Health Inform 2017;21:1562‑72. Lucchinetti C, Bruck W, Parisi J, Scheithauer B, Rodriguez M, Lassmann H. Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination. Ann Neurol 2000;47:707‑17. Conradsson D, Ytterberg C, Engelkes C, Johansson S, Gottberg K. Activity limitations and participation restrictions in people with multiple sclerosis: A detailed 10‑year perspective. Disabil Rehabil 2021;43:406‑13. Goldman MD, Marrie RA, Cohen JA. Evaluation of the six‑minute walk in multiple sclerosis subjects and healthy controls. Mult Scler 2008;14:383‑90. Hayes S, Uszynski MK, Motl RW, Gallagher S, Larkin A, Newell J, et al. Randomised controlled pilot trial of an exercise plus behaviour change intervention in people with multiple sclerosis: The Step it Up study. BMJ Open 2017;7:e016336. Thomas S, Fazakarley L, Thomas PW, Collyer S, Brenton S, Perring S, et al. Mii‑vitaliSe: a pilot randomised controlled trial of a home gaming system (Nintendo Wii) to increase activity levels, vitality and well‑being in people with multiple sclerosis. BMJ Open 2017;7:e016966. Zequera M, Perdomo O, Wilches C, Vizcaya P. Pilot study: Assessing repeatability of the EcoWalk platform resistive pressure sensors to measure plantar pressure during barefoot standing. J Phys Conf Ser 2013;50:1‑4. Fukuda TY, Echeimberg JO, Pompeu JE, Garcia‑Lucareli PR, Garbelotti S, Okano‑Gimenes R, et al. Root mean square value of the electromyographic signal in the isometric torque of the quadriceps, hamstrings and brachial biceps muscles in female subjects. J Appl Res 2010;10:32‑9. Hermens HJ, Freriks B, Disselhorst‑Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 2000;10:361‑74. Petrocci KE, Cárdenas Sandoval RP. La medición del control postural con estabilometría‑ una revisión documental. Rev Colomb Rehabil 2011;10:16‑24. Richman J, Lake D, Moorman JR. Sample entropy. Methods Enzymol 2004;384:172‑84. Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst Appl 2012;39:7420‑31. Perpetuini D, Cardone D, Chiarelli AM, Filippini C, Croce P, Zappasodi F, et al. Autonomic impairment in Alzheimer’s disease is revealed by complexity analysis of functional thermal imaging signals during cognitive tasks. Physiol Meas 2019;40:1‑9. Richman JS, Moorman JR. Physiological time‑series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000;278:H2039‑49. Zhang X, Zhou P. Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes. J Electromyogr Kinesiol 2012;22:901‑7. Perpetuini D, Chiarelli AM, Cardone D, Filippini C, Bucco R, Zito M, et al. Complexity of frontal cortex fNIRS can support Alzheimer disease diagnosis in memory and visuo‑spatial tests. Entropy (Basel) 2019;21:E26. Berger JO. Statistical Decision Theory and Bayesian Analysis. New York: Springer; 1985. Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis. New York: Chapman and Hall/CRC; 2013. Bolstad WM. Introduction to Bayesian Statistics. New Jersey: John Wiley & Sons; 2004. Kruschke JK. Bayesian estimation supersedes the t‑test. J Exp Psychol Gen 2013;142:573‑603. Arpan I, Fino PC, Fling BW, Horak F. Local dynamic stability during long‑fatiguing walks in people with multiple sclerosis. Gait Posture 2020;76:122‑7. Tolbert D. Trunk Stability and Postural Stability in People with Multiple Sclerosis. South Dakota State University; 2018. Gates DH, Dingwell JB. The effects of muscle fatigue and movement height on movement stability and variability. Exp Brain Res 2011;209:525‑36. Kellis E, Galanis N, Kofotolis N, Hatzi A. Effects of hip flexion angle on surface electromyographic activity of the biceps femoris and semitendinosus during isokinetic knee flexion. Muscles Ligaments Tendons J 2017;7:286‑92. Farrell JW 3rd, Motl RW, Learmonth YC, Pilutti LA. Persons with multiple sclerosis exhibit strength asymmetries in both upper and lower extremities. Physiotherapy 2021;111:83‑91. Potvin JR, Fuglevand AJ. A motor unit‑based model of muscle fatigue. PLoS Comput Biol 2017;13:e1005581. Dutta R, Trapp BD. Mechanisms of neuronal dysfunction and degeneration in multiple sclerosis. Prog Neurobiol 2011;93:1‑12. Smith KJ, McDonald WI. Mechanisms of symptom production. In: Blue Books of Practical Neurology. Vol. 27, Ch. 5. Oxford: Elsevier Inc., 2003. p. 59‑74. |
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Henao Pérez, JulietaGomez Hernández, MarinaOlaya Mira, NataliViloria Barragán, CarolinaHenao Pérez, JulietaRojas Mora, Jessica MaríaDíaz Londoño, Gloria2023-06-14T14:24:58Z2023-06-14T14:24:58Z2023-06-09DOI: 10.4103/jmss.jmss_184_21https://hdl.handle.net/20.500.12494/51364Gómez Hernández, M., Olaya Mira, N., Viloria Barragán, C., Henao Pérez, J., Rojas Mora, J. M. y Díaz Londoño, G. (2023). Assessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of concept. [Articulo, Universidad Cooperativa de Colombia]. Repositorio Institucional Universidad Cooperativa de Colombia.https://repository.ucc.edu.co/handle/20.500.12494/51364Background: Multiple sclerosis (MS) is a progressive and neurodegenerative disease of the central nervous system. Its symptoms vary greatly, which makes its diagnosis complex, expensive, and time‑consuming. One of its most prevalent symptoms is muscle fatigue. It occurs in about 92% of patients with MS (PwMS) and is defined as a decrease in maximal strength or energy production in response to contractile activity. This article aims to compare the behavior of a healthy control (HC) with that of a patient with MS before and after muscle fatigue. Methods: For this purpose, a static baropodometric test and a dynamic electromyographic analysis are performed to calculate the area of the stabilometric ellipse, the remitting MS (RMS) value, and the sample entropy (SampEn) of the signals, as a proof of concept to explore the feasibility of this test in the muscle fatigue quantitative analysis; in addition, the statistical analysis was realized to verify the results. Results: According to the results, the ellipse area increased in the presence of muscle fatigue, indicating a decrease in postural stability. Likewise, the RMS value increased in the MS patient and decreased in the HC subject and the opposite behavior in the SampEn was observed in the presence of muscle fatigue. Conclusion: Thus, this study demonstrates that SampEn is a viable parameter to estimate muscle fatigue in PwMS and other neuromuscular diseases.julieta.henaop@campusucc.edu.co1-7 p.Universidad Cooperativa de Colombia, Facultad de Ciencias de la Salud, Medicina, Medellín y EnvigadoMedicinaMedellínhttps://www.jmssjournal.net/text.asp?2023/13/2/153/377810Journal of Medical Signals & SensorsGhasemi N, Razavi S, Nikzad E. Multiple sclerosis: Pathogenesis, symptoms, diagnoses and cell‑based therapy. Cell J 2017;19:1‑10.Goodin DS. The Epidemiology of Multiple Sclerosis: Insights to a Causal Cascade. In: Handbook of Clinical Neurology. 1st ed., Vol. 138. no. 3. Amsterdam: Elsevier B.V., 2016.Nogales‑Gaete J, Aracena R, Cepeda‑Zumaeta S, Eloiza C, Agurto P, Díaz V, et al. Clinical features of 314 patients with relapsing‑remitting multiple sclerosis. Rev Med Chil 2014;142:559‑66.Osorio‑Marcatinco V, Castro‑Suarez S, Meza‑Vega M. Cognitive characteristics of patients with relapsing ‑ remitting multiple sclerosis during relapse attended in National Institute of Neurological Sciences 2014 ‑ 2016. Rev Neuropsiquiatr 2018;81:58‑64.Oh J, Alikhani K, Bruno T, Devonshire V, Giacomini PS, Giuliani F, et al. Diagnosis and management of secondary‑progressive multiple sclerosis: Time for change. Neurodegener Dis Manag 2019;9:301‑17.Olek M, Mowry E. Pathogenesis and epidemiology of multiple sclerosis,” Up To Date; 2020. p. 1‑23. Available: https://www. uptodate.com/contents/pathogenesis‑and‑epidemiology‑of‑ multiple‑sclerosis. [Last cited on 2020 May 06].Leray E, Moreau T, Fromont A, Edan G. Epidemiology of multiple sclerosis. Rev Neurol (Paris) 2016;172:3‑13.Negrotto L, Correale J. Evolution of multiple sclerosis prevalence and phenotype in Latin America. Mult Scler Relat Disord 2018;22:97‑102.Jiménez C, Zarco L, Castañeda C, Otálora M, Martínez A, Rosselli D. Current state of multiple sclerosis in Colombia. Acta Neurol Colomb 2015;31:385‑90.Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018;17:162‑73.Diazgranados Sánchez JA, Burbano J, Herrera Escandón Á, Hidalgo JF, Gómez Betancourt LF, Chan Guevara L. Mc Donald 2010 criteria in the diagnosis of multiple sclerosis in Cali, Colombia. Acta Neurol Colomb 2013;29:247‑54.Jarmi V, De Elías R, Kiener O, Villate S, Vrech C, Barzón S. Oligoclonal bands: contribution and interpretation in patients with suspected multiple sclerosis. Acta Bioquim Clin Latinoam 2015;49:257‑65.Rojas Huerto E, Alva Diaz C, Montalvan Ayala V. Clinical changes of multiple sclerosis according to modification of the McDonald criteria. Hospital Almenara, 2001‑2015. An la Fac Med 2019;80:34‑8.Martinez‑Altarriba MC, Ramos‑Campoy O, Luna‑Calcaño IM, Arrieta‑Antón E. A review of multiple sclerosis (2). Diagnosis and treatment. Semergen 2015;41:324‑8.Castellano‑Del Castillo MA, Lacasa‑Andrade ME, Hijós‑Bitrián E, Mambrona‑Girón L, Sebastiá‑Vigatá E, Vázquez‑Sasot A. Effectiveness of rehabilitation in multiple sclerosis. Rehabilitacion 2014;48:46‑53.Yusuf F, Wijnands JM, Kingwell E, Zhu F, Evans C, Fisk JD, et al. Fatigue, sleep disorders, anaemia and pain in the multiple sclerosis prodrome. Mult Scler 2021;27:290‑302.Proessl F, Poston B, Rudroff T. Does a single application of anodal tDCS improve knee extensor fatigability in people with multiple sclerosis? Brain Stimul 2018;11:1388‑90.Newland P, Van Aman MN, Smith J, Spencer A. Relationship of fatigue to heat sensitivity in patients with multiple sclerosis: A review for management. J Nurse Pract 2018;14:100‑4.Sumowski JF, Leavitt VM. Body temperature is elevated and linked to fatigue in relapsing‑remitting multiple sclerosis, even without heat exposure. Arch Phys Med Rehabil 2014;95:1298‑302.Eken MM, Richards R, Beckerman H, van der Krogt M, Gerrits K, Rietberg M, et al. Quantifying muscle fatigue during walking in people with multiple sclerosis. Clin Biomech (Bristol, Avon) 2020;72:94‑101.Beretta‑Piccoli M, Cescon C, Barbero M, Villiger M, Clijsen R, Kool J, et al. Upper and lower limb performance fatigability in people with multiple sclerosis investigated through surface electromyography: A pilot study. Physiol Meas 2020;41:025002.Zhang X, Wang D, Yu Z, Chen X, Li S, Zhou P. EMG‑torque relation in chronic stroke: A novel EMG complexity representation with a linear electrode array. IEEE J Biomed Health Inform 2017;21:1562‑72.Lucchinetti C, Bruck W, Parisi J, Scheithauer B, Rodriguez M, Lassmann H. Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination. Ann Neurol 2000;47:707‑17.Conradsson D, Ytterberg C, Engelkes C, Johansson S, Gottberg K. Activity limitations and participation restrictions in people with multiple sclerosis: A detailed 10‑year perspective. Disabil Rehabil 2021;43:406‑13.Goldman MD, Marrie RA, Cohen JA. Evaluation of the six‑minute walk in multiple sclerosis subjects and healthy controls. Mult Scler 2008;14:383‑90.Hayes S, Uszynski MK, Motl RW, Gallagher S, Larkin A, Newell J, et al. Randomised controlled pilot trial of an exercise plus behaviour change intervention in people with multiple sclerosis: The Step it Up study. BMJ Open 2017;7:e016336.Thomas S, Fazakarley L, Thomas PW, Collyer S, Brenton S, Perring S, et al. Mii‑vitaliSe: a pilot randomised controlled trial of a home gaming system (Nintendo Wii) to increase activity levels, vitality and well‑being in people with multiple sclerosis. BMJ Open 2017;7:e016966.Zequera M, Perdomo O, Wilches C, Vizcaya P. Pilot study: Assessing repeatability of the EcoWalk platform resistive pressure sensors to measure plantar pressure during barefoot standing. J Phys Conf Ser 2013;50:1‑4.Fukuda TY, Echeimberg JO, Pompeu JE, Garcia‑Lucareli PR, Garbelotti S, Okano‑Gimenes R, et al. Root mean square value of the electromyographic signal in the isometric torque of the quadriceps, hamstrings and brachial biceps muscles in female subjects. J Appl Res 2010;10:32‑9.Hermens HJ, Freriks B, Disselhorst‑Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 2000;10:361‑74.Petrocci KE, Cárdenas Sandoval RP. La medición del control postural con estabilometría‑ una revisión documental. Rev Colomb Rehabil 2011;10:16‑24.Richman J, Lake D, Moorman JR. Sample entropy. Methods Enzymol 2004;384:172‑84.Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst Appl 2012;39:7420‑31.Perpetuini D, Cardone D, Chiarelli AM, Filippini C, Croce P, Zappasodi F, et al. Autonomic impairment in Alzheimer’s disease is revealed by complexity analysis of functional thermal imaging signals during cognitive tasks. Physiol Meas 2019;40:1‑9.Richman JS, Moorman JR. Physiological time‑series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000;278:H2039‑49.Zhang X, Zhou P. Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes. J Electromyogr Kinesiol 2012;22:901‑7.Perpetuini D, Chiarelli AM, Cardone D, Filippini C, Bucco R, Zito M, et al. Complexity of frontal cortex fNIRS can support Alzheimer disease diagnosis in memory and visuo‑spatial tests. Entropy (Basel) 2019;21:E26.Berger JO. Statistical Decision Theory and Bayesian Analysis. New York: Springer; 1985.Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis. 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Oxford: Elsevier Inc., 2003. p. 59‑74.BaropodometriaMuestra de entropíaFatiga muscularEsclerosis multipleElectromiografíaBaropodometrySample entropyMuscle fatigueMultiple sclerosisElectromyographyAssessing muscle fatigue in multiple sclerosis using the sample entropy of electromyographic signals: a proof of conceptArtículohttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionNINGUNAinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PublicationORIGINAL2023_assessing_muscle_fatigue-FormatoLicenciaUso.pdf2023_assessing_muscle_fatigue-FormatoLicenciaUso.pdfapplication/pdf236833https://repository.ucc.edu.co/bitstreams/9e054e63-5dfc-4fa4-b114-72fc7a6a5ba4/download58dc9df3d3d02cffd64f504ef356eb06MD51LICENSElicense.txtlicense.txttext/plain; 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