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Time-frequency representations from inertial sensors to characterize the gait in Parkinson’s disease

Representaciones tiempo-frecuencia basadas en sensores inerciales para caracterizar la marcha en la enfermedad de Parkinson

dc.creatorBedoya-Vargas, Marlon E.
dc.creatorVásquez-Correa, Juan C.
dc.creatorOrozco-Arroyave, Juan R.
dc.date2018-09-14
dc.date.accessioned2021-03-18T21:11:20Z
dc.date.available2021-03-18T21:11:20Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1056
dc.identifier10.22430/22565337.1056
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/11746
dc.descriptionParkinson’s Disease (PD) is a neurodegenerative disorder of the central nervous system whose main symptoms include rigidity, bradykinesia, and loss of postural reflexes. PD diagnosis is based on an analysis of the medical record and physical examinations of the patient. Besides, the neurological state of patients is monitored with subjective evaluations by neurologists. Gait analysis using inertial sensors was introduced as a simple and useful tool that supports the diagnosis and monitoring of PD patients. This work used the eGaIT system to capture the signals of the accelerometer and the gyroscope of the gait in order to evaluate the motor skills of patients. Fourier and wavelet transform were used to extract measurements based on energy and entropy in the time-frequency domain. The extracted characteristics were used to recognize differences between PD patients and healthy individuals. The results enabled to classify said groups with an accuracy of up to 94%.en-US
dc.descriptionLa Enfermedad de Parkinson (EP) es un desorden neurodegenerativo del sistema nervioso central, cuyas características principales incluyen entre otras la rigidez, bradicinesia y pérdida de los reflejos posturales. El diagnóstico de la EP está basado en análisis de la historia clínica y evaluaciones físicas realizadas a los pacientes. El monitoreo del estado neurológico de los pacientes está basado en valoraciones subjetivas que realizan los neurólogos. El análisis de la marcha usando sensores inerciales aparece como un instrumento sencillo y útil para ayudar en el proceso de diagnóstico y monitoreo de los pacientes con EP. En este artículo usamos el sistema eGaIT, el cual captura señales de acelerómetro y giróscopo del proceso de marcha para evaluar las habilidades motoras de los pacientes. Las transformadas de Fourier y Wavelet son utilizadas para extraer medidas basadas en energía y entropía en el dominio de Tiempo-Frecuencia. Las características extraídas son utilizadas para discriminar entre pacientes con EP y personas sanas. De acuerdo con los resultados, es posible clasificar estos dos grupos con una precisión de hasta el 94 %.es-ES
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dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1056/1064
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1056/1079
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1056/1216
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1056/1237
dc.relation/*ref*/S. Sveinbjornsdottir, “The clinical symptoms of Parkinson’s disease,” J. Neurochem., vol. 139, pp. 318–324, Oct. 2016. [2] A. A. Moustafa et al., “Motor symptoms in Parkinson’s disease: A unified framework,” Neurosci. Biobehav. Rev., vol. 68, pp. 727–740, Sep. 2016. [3] C. G. Goetz et al., “Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results,” Mov. Disord., vol. 23, no. 15, pp. 2129–2170, Nov. 2008. [4] R. B. Postuma et al., “MDS clinical diagnostic criteria for Parkinson’s disease,” Mov. Disord., vol. 30, no. 12, pp. 1591–1601, Oct. 2015. [5] M. E. Morris, F. Huxham, J. McGinley, K. Dodd, and R. Iansek, “The biomechanics and motor control of gait in Parkinson disease,” Clin. Biomech., vol. 16, no. 6, pp. 459–470, Jul. 2001. [6] J. Barth et al., “Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson’s disease,” in International Conference of the Engineering in Medicine and Biology Society (EMBC), 2012, pp. 5122–5125. [7] B. Mariani, M. C. Jiménez, F. J. G. Vingerhoets, and K. Aminian, “On-Shoe Wearable Sensors for Gait and Turning Assessment of Patients With Parkinson’s Disease,” IEEE Trans. Biomed. Eng., vol. 60, no. 1, pp. 155–158, Jan. 2013. [8] E. Sejdic, K. A. Lowry, J. Bellanca, M. S. Redfern, and J. S. Brach, “A Comprehensive Assessment of Gait Accelerometry Signals in Time, Frequency and Time-Frequency Domains,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 3, pp. 603–612, May 2014. [9] J. C. M. Schlachetzki et al., “Wearable sensors objectively measure gait parameters in Parkinson’s disease,” PLoS One, vol. 12, no. 10, p. e0183989, Oct. 2017. [10] J. C. Vásquez-Correa et al., “Multi-view representation learning via gcca for multimodal analysis of Parkinson’s disease,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 2966–2970. [11] A. Martínez-Ramírez et al., “Frailty assessment based on trunk kinematic parameters during walking,” J. Neuroeng. Rehabil., vol. 12, no. 1, p. 48, Dec. 2015. [12] B. Boashash, “Time-frequency signal analysis and processing: a comprehensive reference,” in Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, 2nd ed., Academic Press, 2015, pp. 65–100. [13] J. R. Orozco-Arroyave et al., “NeuroSpeech: An open-source software for Parkinson’s speech analysis,” Digit. Signal Process., vol. 77, pp. 207–221, Jun. 2018. [14] L. Cohen, “Time-frequency distributions-a review,” Proc. IEEE, vol. 77, no. 7, pp. 941–981, Jul. 1989. [15] S. Mallat, A wavelet tour of signal processing: the sparse way, 3rd ed. Academic press, 2008. [16] C. K. Chui, An introduction to wavelets. Academic Press, 2016. [17] T. Arias-Vergara, J. C. Vásquez-Correa, and J. R. Orozco-Arroyave, “Parkinson’s Disease and Aging: Analysis of Their Effect in Phonation and Articulation of Speech,” Cognit. Comput., vol. 9, no. 6, pp. 731–748, Dec. 2017
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/en-US
dc.sourceTecnoLógicas; Vol. 21 No. 43 (2018); 53-69en-US
dc.sourceTecnoLógicas; Vol. 21 Núm. 43 (2018); 53-69es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectParkinson’s Diseaseen-US
dc.subjectinertial sensorsen-US
dc.subjecttime-frequency representationen-US
dc.subjectwavelet transformen-US
dc.subjectgait analysisen-US
dc.subjectsupervised classificationen-US
dc.subjectEnfermedad de Parkinsones-ES
dc.subjectsensores inercialeses-ES
dc.subjectrepresentación tiempo-frecuenciaes-ES
dc.subjectTransformada Waveletes-ES
dc.subjectanálisis de marchaes-ES
dc.subjectclasificación supervisadaes-ES
dc.titleTime-frequency representations from inertial sensors to characterize the gait in Parkinson’s diseaseen-US
dc.titleRepresentaciones tiempo-frecuencia basadas en sensores inerciales para caracterizar la marcha en la enfermedad de Parkinsones-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeArticlesen-US
dc.typeArtículoses-ES


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