Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns

Abstract: Traditionally, the Parkinson Disease (PD) is diagnosed and followed up by conventional clinical tests that are fully dependent on the expert experience. The diffuse boundary between normal and early parkinson stages and the high variability of gait patterns difficult any objective characte...

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Autores:
Sarmiento Castillo, Fernanda Carolina
Tipo de recurso:
Fecha de publicación:
2015
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/56740
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/56740
http://bdigital.unal.edu.co/52661/
Palabra clave:
61 Ciencias médicas; Medicina / Medicine and health
62 Ingeniería y operaciones afines / Engineering
Parkinson Disease
Kinematic
Gait Patterns
Ipsilateral Coordination
RPI
PERP
SVM
Enfermedad de Parkinson
Cinemática
Patrones de marcha
Coordinación ipsilateral
Rights
restrictedAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_2e6a325a63bd67f3bd1b3ac6d9013bab
oai_identifier_str oai:repositorio.unal.edu.co:unal/56740
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns
title Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns
spellingShingle Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns
61 Ciencias médicas; Medicina / Medicine and health
62 Ingeniería y operaciones afines / Engineering
Parkinson Disease
Kinematic
Gait Patterns
Ipsilateral Coordination
RPI
PERP
SVM
Enfermedad de Parkinson
Cinemática
Patrones de marcha
Coordinación ipsilateral
title_short Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns
title_full Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns
title_fullStr Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns
title_full_unstemmed Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns
title_sort Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns
dc.creator.fl_str_mv Sarmiento Castillo, Fernanda Carolina
dc.contributor.author.spa.fl_str_mv Sarmiento Castillo, Fernanda Carolina
dc.contributor.spa.fl_str_mv Romero Castro, Eduardo
dc.subject.ddc.spa.fl_str_mv 61 Ciencias médicas; Medicina / Medicine and health
62 Ingeniería y operaciones afines / Engineering
topic 61 Ciencias médicas; Medicina / Medicine and health
62 Ingeniería y operaciones afines / Engineering
Parkinson Disease
Kinematic
Gait Patterns
Ipsilateral Coordination
RPI
PERP
SVM
Enfermedad de Parkinson
Cinemática
Patrones de marcha
Coordinación ipsilateral
dc.subject.proposal.spa.fl_str_mv Parkinson Disease
Kinematic
Gait Patterns
Ipsilateral Coordination
RPI
PERP
SVM
Enfermedad de Parkinson
Cinemática
Patrones de marcha
Coordinación ipsilateral
description Abstract: Traditionally, the Parkinson Disease (PD) is diagnosed and followed up by conventional clinical tests that are fully dependent on the expert experience. The diffuse boundary between normal and early parkinson stages and the high variability of gait patterns difficult any objective characterization of this disease. An automatic characterization of the PD is herein proposed by mixing up different measures of the ipsilateral coordination and spatiotemporal gait patterns which are then classified with a classical support vector machine (SVM). The strategy was evaluated in a population with parkinson and healthy control subjects, obtaining an average accuracy of 87% for the task of classification. The second approximation was developed under the rule that the ipsilateral coordination disturbances reflect the general motor control deficit described in PD, so that can be used in the objective characterization of the disease. Two ipsilateral coordination measures have been widely used in the identification of their patterns, the Relative Power Index (RPI) and the Point Estimates of Relative Phase (PERP). In this paper we look into the potential use of ipsilateral coordination patterns for the automatic characterization of the PD, therefore is proposed a comparative accuracy analysis of the RPI and PERP for the classification of the interest groups by a classical SVM. The strategy was evaluated in a population with parkinson (16 subjects) and healthy control subjects (7), obtaining an average accuracy of 94,6% and 82,1%, for PERP and RPI respectively.
publishDate 2015
dc.date.issued.spa.fl_str_mv 2015-12-07
dc.date.accessioned.spa.fl_str_mv 2019-07-02T12:05:04Z
dc.date.available.spa.fl_str_mv 2019-07-02T12:05:04Z
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 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/56740
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/52661/
url https://repositorio.unal.edu.co/handle/unal/56740
http://bdigital.unal.edu.co/52661/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Sede Bogotá Facultad de Medicina Centro de Telemedicina
Centro de Telemedicina
dc.relation.references.spa.fl_str_mv Sarmiento Castillo, Fernanda Carolina (2015) Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns. Maestría thesis, Universidad Nacional de Colombia-Sede Bogota.
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/restrictedAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_16ec
eu_rights_str_mv restrictedAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/56740/1/52887512.2015.pdf
https://repositorio.unal.edu.co/bitstream/unal/56740/2/52887512.2015.pdf.jpg
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repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/access_right/c_16ecRomero Castro, EduardoSarmiento Castillo, Fernanda Carolina24916bc0-b91d-4eae-9659-5975379491a83002019-07-02T12:05:04Z2019-07-02T12:05:04Z2015-12-07https://repositorio.unal.edu.co/handle/unal/56740http://bdigital.unal.edu.co/52661/Abstract: Traditionally, the Parkinson Disease (PD) is diagnosed and followed up by conventional clinical tests that are fully dependent on the expert experience. The diffuse boundary between normal and early parkinson stages and the high variability of gait patterns difficult any objective characterization of this disease. An automatic characterization of the PD is herein proposed by mixing up different measures of the ipsilateral coordination and spatiotemporal gait patterns which are then classified with a classical support vector machine (SVM). The strategy was evaluated in a population with parkinson and healthy control subjects, obtaining an average accuracy of 87% for the task of classification. The second approximation was developed under the rule that the ipsilateral coordination disturbances reflect the general motor control deficit described in PD, so that can be used in the objective characterization of the disease. Two ipsilateral coordination measures have been widely used in the identification of their patterns, the Relative Power Index (RPI) and the Point Estimates of Relative Phase (PERP). In this paper we look into the potential use of ipsilateral coordination patterns for the automatic characterization of the PD, therefore is proposed a comparative accuracy analysis of the RPI and PERP for the classification of the interest groups by a classical SVM. The strategy was evaluated in a population with parkinson (16 subjects) and healthy control subjects (7), obtaining an average accuracy of 94,6% and 82,1%, for PERP and RPI respectively.La Enfermedad de Parkinson (EP) se diagnostica cotidianamente a través de pruebas clínicas convencionales, altamente dependientes de la experiencia del examinador. Debido al límite difuso existente entre los estadios normales y las etapas iniciales de la enfermedad, así como a la alta variabilidad de los patrones de marcha en estos pacientes, la caracterización objetiva de la EP es difícil en la práctica clínica habitual. A partir de ello, se propuso como primera aproximación a la solución de este problema, una estrategia de caracterización automática de la EP basada en la combinación de diferentes medidas de coordinación ipsilateral y patrones espacio-temporales de la marcha, las cuáles hicieron parte de un vector de características ingresado en una máquina de soporte vectorial (SVM) clásica, obteniendo con ello una exactitud promedio del 87,0% en la detección de patrones de marcha de sujetos sanos y con diagnóstico de EP. Tomando como principio que las alteraciones de la coordinación ipsilateral reflejan los déficit de control motor descritos en la EP, de modo que pueden ser empleadas en la caracterización objetiva de la enfermedad desde la perspectiva motora, se desarrolló la segunda estrategia de aproximación a la solución del problema planteado. Dos medidas de coordinación ipsilateral, ampliamente utilizadas en la identificación de sus patrones, conocidas como el Indice de Poder Relativo (RPI) y las Estimaciones Puntuales de la Fase Relativa (PERP), fueron las empleadas en esta estrategia. En virtud de lo previamente indicado, se propuso un análisis comparativo de RPI y PERP para la clasificación de patrones de marcha en sujetos sanos y con diagnóstico de EP, empleando nuevamente una SVM clásica. La estrategia se evaluó con datos cinemáticos de la marcha de 16 sujetos con EP y 7 sujetos sanos, obteniendo una exactitud promedio de 94,6% y 82,1%, para PERP y RPI respectivamente.Maestríaapplication/pdfspaUniversidad Nacional de Colombia Sede Bogotá Facultad de Medicina Centro de TelemedicinaCentro de TelemedicinaSarmiento Castillo, Fernanda Carolina (2015) Automatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait Patterns. Maestría thesis, Universidad Nacional de Colombia-Sede Bogota.61 Ciencias médicas; Medicina / Medicine and health62 Ingeniería y operaciones afines / EngineeringParkinson DiseaseKinematicGait PatternsIpsilateral CoordinationRPIPERPSVMEnfermedad de ParkinsonCinemáticaPatrones de marchaCoordinación ipsilateralAutomatic Characterization of the Parkinson Disease by Classifying the Kinematic Gait PatternsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMORIGINAL52887512.2015.pdfapplication/pdf4330063https://repositorio.unal.edu.co/bitstream/unal/56740/1/52887512.2015.pdfff0f8110c34dfdf39ab3f7169c96b6fcMD51THUMBNAIL52887512.2015.pdf.jpg52887512.2015.pdf.jpgGenerated Thumbnailimage/jpeg4518https://repositorio.unal.edu.co/bitstream/unal/56740/2/52887512.2015.pdf.jpge7408af549f2ab4e542e89a47d18694eMD52unal/56740oai:repositorio.unal.edu.co:unal/567402023-03-19 23:04:34.48Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co