Gait characterizations in Parkinson's disease

ilustraciones, graficas

Autores:: Ricaurte, David Leonardo

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Gait characterizations in Parkinson's disease
dc.title.translated.spa.fl_str_mv	Caracterizaciones de la marcha en la enfermedad de Parkinson
title	Gait characterizations in Parkinson's disease
spellingShingle	Gait characterizations in Parkinson's disease 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Aprendizaje Profundo Ganglios Basales Trastornos Motores Deep Learning Basal Ganglia Motor Disorders Parkinson's disease Chaos Lyapunov exponent Dynamical system Gait Motor control Enfermedad de Parkinson Caos Exponente de Lyapunov Sistema dinámico Marcha Control Motor
title_short	Gait characterizations in Parkinson's disease
title_full	Gait characterizations in Parkinson's disease
title_fullStr	Gait characterizations in Parkinson's disease
title_full_unstemmed	Gait characterizations in Parkinson's disease
title_sort	Gait characterizations in Parkinson's disease
dc.creator.fl_str_mv	Ricaurte, David Leonardo
dc.contributor.advisor.none.fl_str_mv	Romero Castro, Edgar Eduardo
dc.contributor.author.none.fl_str_mv	Ricaurte, David Leonardo
dc.contributor.researchgroup.spa.fl_str_mv	Cim@Lab
dc.subject.ddc.spa.fl_str_mv	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Aprendizaje Profundo Ganglios Basales Trastornos Motores Deep Learning Basal Ganglia Motor Disorders Parkinson's disease Chaos Lyapunov exponent Dynamical system Gait Motor control Enfermedad de Parkinson Caos Exponente de Lyapunov Sistema dinámico Marcha Control Motor
dc.subject.other.spa.fl_str_mv	Aprendizaje Profundo Ganglios Basales Trastornos Motores
dc.subject.other.eng.fl_str_mv	Deep Learning Basal Ganglia Motor Disorders
dc.subject.proposal.eng.fl_str_mv	Parkinson's disease Chaos Lyapunov exponent Dynamical system Gait Motor control
dc.subject.proposal.spa.fl_str_mv	Enfermedad de Parkinson Caos Exponente de Lyapunov Sistema dinámico Marcha Control Motor
description	ilustraciones, graficas
publishDate	2022
dc.date.issued.none.fl_str_mv	2022
dc.date.accessioned.none.fl_str_mv	2023-01-24T13:11:55Z
dc.date.available.none.fl_str_mv	2023-01-24T13:11:55Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	DataPaper Image Model Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/83080
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/83080 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	[1] G. DeMaagd and A. Philip, “Parkinson’s disease and its management: part 1: disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis,” Pharmacy and therapeutics, vol. 40, no. 8, p. 504, 2015 [2] K. McFarthing, G. Rafaloff, M. A. Baptista, R. K. Wyse, and S. R. Stott, “Parkinson’s disease drug therapies in the clinical trial pipeline: 2021 update,” Journal of Parkinson’s Disease, vol. 11, no. 3, p. 891, 2021 [3] E. Dorsey, T. Sherer, M. S. Okun, and B. R. Bloem, “The emerging evidence of the parkinson pandemic,” Journal of Parkinson’s disease, vol. 8, no. s1, pp. S3–S8, 2018. [4] C. Váradi, “Clinical features of parkinson’s disease: the evolution of critical symptoms,” Biology, vol. 9, no. 5, p. 103, 2020. [5] C. G. Goetz, “The history of parkinson’s disease: early clinical descriptions and neurolo- gical therapies,” Cold Spring Harbor perspectives in medicine, vol. 1, no. 1, p. a008862, 2011. [6] P. C. Poortvliet, A. Gluch, P. A. Silburn, and G. D. Mellick, “The queensland parkin- son’s project: an overview of 20 years of mortality from parkinson’s disease,” Journal of Movement Disorders, vol. 14, no. 1, p. 34, 2021. [7] S. Rong, G. Xu, B. Liu, Y. Sun, L. G. Snetselaar, R. B. Wallace, B. Li, J. Liao, and W. Bao, “Trends in mortality from parkinson disease in the united states, 1999–2019,” Neurology, vol. 97, no. 20, pp. e1986–e1993, 2021. [8] F. Yang, A. L. Johansson, N. L. Pedersen, F. Fang, M. Gatz, and K. Wirdefeldt, “Socio- economic status in relation to parkinson’s disease risk and mortality: A population-based prospective study,” Medicine, vol. 95, no. 30, 2016 [9] J. Jankovic and E. K. Tan, “Parkinson’s disease: Etiopathogenesis and treatment,” Journal of Neurology, Neurosurgery & Psychiatry, vol. 91, no. 8, pp. 795–808, 2020. [10] P. Martinez-Martin, M. Skorvanek, J. M. Rojo-Abuin, Z. Gregova, G. T. Stebbins, C. G. Goetz, and Q. S. Group, “Validation study of the hoehn and yahr scale included in the mds-updrs,” Movement Disorders, vol. 33, no. 4, pp. 651–652, 2018. [11] N. I. of Health et al., “Parkinson’s disease: challenges, progress, and promise,” 2009. [12] K. A. Al Mamun, M. Alhussein, K. Sailunaz, and M. S. Islam, “Cloud based frame- work for parkinson’s disease diagnosis and monitoring system for remote healthcare applications,” Future Generation Computer Systems, vol. 66, pp. 36–47, 2017. [13] A. Tekriwal, D. S. Kern, J. Tsai, N. F. Ince, J. Wu, J. A. Thompson, and A. Abosch, “Rem sleep behaviour disorder: prodromal and mechanistic insights for parkinson’s di- sease,” Journal of Neurology, Neurosurgery & Psychiatry, vol. 88, no. 5, pp. 445–451, 2017. [14] M. Gerlach, W. Maetzler, K. Broich, H. Hampel, L. Rems, T. Reum, P. Riederer, A. St ̈offler, J. Streffer, and D. Berg, “Biomarker candidates of neurodegeneration in parkinson’s disease for the evaluation of disease-modifying therapeutics,” Journal of neural transmission, vol. 119, no. 1, pp. 39–52, 2012. [15] P. P. Michel, E. C. Hirsch, and S. Hunot, “Understanding dopaminergic cell death pathways in parkinson disease,” Neuron, vol. 90, no. 4, pp. 675–691, 2016. [16] A. Iarkov, G. Barreto, J. Grizzell, and V. Echeverria, “Strategies for the treatment of parkinson’s disease: beyond dopamine. front. aging neurosci. 2020; 12: 4,” 2020. [17] D. A. Winter, Biomechanics and motor control of human movement. John Wiley & Sons, 2009. [18] D. Grabli, C. Karachi, M.-L. Welter, B. Lau, E. C. Hirsch, M. Vidailhet, and C. Fran ̧cois, “Normal and pathological gait: what we learn from parkinson’s disease,” Journal of Neurology, Neurosurgery & Psychiatry, vol. 83, no. 10, pp. 979–985, 2012. [19] A. Alamdari and V. N. 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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Romero Castro, Edgar Eduardod49b2499bdf2c07e42f8b4dc9715ef18Ricaurte, David Leonardo9a0abc947cdfe0d09f7dbace27ea5e11Cim@Lab2023-01-24T13:11:55Z2023-01-24T13:11:55Z2022https://repositorio.unal.edu.co/handle/unal/83080Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficasLa enfermedad de Parkinson (EP) es una enfermedad neurodegenerativa que afecta el sistema de control motor encargado de los movimientos voluntarios del cuerpo humano y las funciones cognitivas. EP es la segunda enfermedad neurodegenerativa m mas común después de la enfermedad de Alzhaimer con una población mundial aproximada de 6 millones y con un estimado de 18 millones de personas para el año 2040. Se caracteriza por la muerte de las neuronas dopaminérgicas en un área conocida como substancia nigra pars compacta, que afecta directamente la función de los ganglios basales, afectando el sistema de control motor. Las principales manifestaciones motoras que se presentan debido a EP son bradicinesia, hipocinesia, alteración del equilibrio y de la marcha. Además, la EP afecta la capacidad de aprendizaje movimientos y de tareas repetitivas. Debido a las limitaciones funcionales de los movimientos que se presentan durante la progresión de la enfermedad, se han diseñado tratamientos invasivos (quirúrgicos) y no invasivos (medicamentos) para mejorar la calidad de vida de los pacientes. Los trastornos motores en la EP muestran una alta variabilidad interindividual que desafía las estrategias actuales basadas en la observación en el entorno clínico para determinar la evolución real de la enfermedad y monitorear la respuesta a la terapia. Diferentes investigaciones han intentado analizar cuantitativamente los patrones de marcha por métodos lineales enfrentando varias limitaciones debido a la naturaleza no estacionaria de los patrones de marcha. Sin embargo, esa variabilidad contiene patrones ocultos que no son fácilmente cuantificables en una rutina clínica por su alta complejidad. Debido a que el patrón de marcha podría abordarse como un sistema caótico determinista, es posible asociar a individuos sanos con un alto comportamiento caótico (complejidad) y las anormalidades de la marcha presentes en pacientes con EP, se puede asociar con uno menor (menor complejidad). En el presente trabajo se desarrolló en dos partes. La primera parte consistió en la caracterización no lineal de la marcha de la EP mediante un análisis caótico determinista que representa la dinámica temporal de la marcha con un conjunto mínimo de parámetros. Específicamente, se obtuvieron parámetros retardo (delay) y dimensión embebida para reconstruir el espacio de fase y sus coeficientes característicos, a saber, exponente de Lyapunov, dimensión de correlacion y entropía aproximada. Se encontraron diferencias estadísticas (p < 0.05, prueba de Mann-Whitney) para el exponente de Lyapunov y la entropía aproximada al describir los patrones de marcha de los grupos control y EP. La segunda parte de este trabajo tuvo como objetivo representar de forma no lineal la cinemateca de las extremidades inferiores, destacando las diferencias entre los estadios de la EP. Para ello, se incluyó pose estimation basado en aprendizaje profundo para obtener los puntos de referencia del cuerpo y sus series temporales y, posteriormente, construir el espacio de fase basado en sus derivadas. Luego se calculó el mayor exponente de Lyapunov, la dimensión de correlación y la entropía aproximada, dando como resultado diferencias estadísticamente significativas (Prueba de rango Kruskal-Wallis, p < 0.05), particularmente entre los controles sanos y las etapas 3, la etapa más avanzada, y al comparar el estadio 1 frente al estadio 3. Estos hallazgos brindan información sobre como los patrones complejos pueden estar relacionados con la progresión de la enfermedad en la EP y pueden implementarse fácilmente utilizando dispositivos de video RGB asequibles (Texto tomado de la fuente)Parkinson’s disease (PD) is a neurodegenerative disease that affects the motor control system responsible for the voluntary movements of the human body and cognitive functions. PD is the second most common neurodegenerative disease after Alzheimer’s disease with a world population of approximately 6 million and an estimated 18 million people by 2040. It is characterized by the death of dopaminergic neurons in an area known as substantia nigra pars compacta, which directly affects the function of the basal ganglia, affecting the motor control system. Motor manifestations include bradykinesia, hypokinesia, balance and gait disturbance. In addition, PD affects the ability to learn movements and repetitive tasks. Due to the functional limitations of movements that occur during the progression of the disease, invasive (surgical) and non-invasive (medication) treatments have been designed to improve the quality of life of patients. Motor disorders in PD show high inter-individual variability that challenges current observation-based strategies in the clinical setting to determine the current course of the disease and monitor response to therapy. Several researchers have attempted to quantitatively analyze gait patterns by linear methods, facing several limitations due to the non-stationary nature of gait patterns. However, this variability contains hidden patterns that are not easily quantifiable in a clinical routine and are highly complex. Because of the gait pattern could be approached as a deterministic chaotic system, it is possible to associate healthy individuals with high chaotic behavior and the gait abnormalities present in PD patients can be associated with decreased chaotic behavior. This work is developed in two parts. The first part consisted in making a non-linear characterization of the PD gait by means of a deterministic chaotic analysis that represents the temporal gait dynamics with a minimum set of parameters. Specifically, embedding and delay dimension parameters were obtained to reconstruct the phase space and its characteristic coefficients, namely Lyapunov exponent, correlation dimension, and approximate entropy. Statistical differences (p < 0,05, Mann-Whitney test) were found for the Lyapunov exponent and the approximate entropy when describing two gait patterns, that is, the control and PD groups. The second part of this work aimed to represent the kinematics of the lower extremities in a non-linear way, highlighting the differences between the stages of PD. For this, a widely used deep learning framework was implemented to obtain the reference points of the body and its time series and subsequently build the phase space based on the first-order derivatives. The largest Lyapunov exponent, correlation dimension, and approximate entropy were then calculated, resulting in statistically significant differences (Kruskal-Wallis rank test, p < 0,05), particularly between the healthy controls and stage 3, the most advanced stage, and compare stage 1 versus stage 3. These findings provide insight into how complex patterns may be related to disease progression in PD and can be easily implemented using imaging devices like RGB video capture.MaestríaMagíster en Ingeniería BiomédicaMotion and Biosignal Analysisxiv, 48 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Medicina - Maestría en Ingeniería BiomédicaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaAprendizaje ProfundoGanglios BasalesTrastornos MotoresDeep LearningBasal GangliaMotor DisordersParkinson's diseaseChaosLyapunov exponentDynamical systemGaitMotor controlEnfermedad de ParkinsonCaosExponente de LyapunovSistema dinámicoMarchaControl MotorGait characterizations in Parkinson's diseaseCaracterizaciones de la marcha en la enfermedad de ParkinsonTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionDataPaperImageModelTexthttp://purl.org/redcol/resource_type/TM[1] G. 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Stergiou, “The appropriate use of approximate entropy and sample entropy with short data sets,” Annals of biomedical engineering, vol. 41, no. 2, pp. 349–365, 2013.EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83080/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL80927859.2022.pdf80927859.2022.pdfTesis de Maestría en Ingeniería Biomédicaapplication/pdf2872570https://repositorio.unal.edu.co/bitstream/unal/83080/4/80927859.2022.pdf0a93566ec7514b0f67ec2b4be2d19be6MD54THUMBNAIL80927859.2022.pdf.jpg80927859.2022.pdf.jpgGenerated Thumbnailimage/jpeg3993https://repositorio.unal.edu.co/bitstream/unal/83080/5/80927859.2022.pdf.jpg835793dd7e83de46b62fcbf87edeef26MD55unal/83080oai:repositorio.unal.edu.co:unal/830802024-08-15 23:15:05.337Repositorio Institucional Universidad Nacional de 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