Using Machine Learning Algorithms for Neurodegenerative Disease Gait Classification
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Cárdenas Torres, A. M., Ealo Otero, L. C., Uribe Perez, J., & Gomez Gomez, B. L. (2023). Using Machine Learning Algorithms for Neurodegenerative Disease Gait Classification. Ingenierías USBmed, 14(2), 8–14. https://doi.org/10.21500/20275846.6081
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Abstract
La detección de los síntomas de las enfermedades neurodegenerativas suele producirse en las últimas fases de la enfermedad, por lo que una detección temprana ayudaría a mejorar la calidad de vida del paciente. La base de datos PhysioNet proporciona información sobre la biomecánica de pacientes con la enfermedad de Parkinson (EP), la esclerosis lateral amiotrófica (ELA) y la enfermedad de Huntington (EH). En este trabajo se utilizan datos espacio-temporales para medir el coste energético y la densidad espectral de potencia en estas patologías. Se utilizan técnicas de c-medias difusas, algoritmo de aprendizaje para el análisis de datos multivariados - LAMDA, y redes neuronales para clasificar datos de marcha de voluntarios con enfermedades neurodegenerativas y un grupo de control. Se entrenaron clasificadores de dos clases: Ctrl+PD, Ctrl+PD y Ctrl+HD. El emparejamiento mejoró el ajuste de LAMDA con un 98,3%, el de la red neuronal con un 97,0% y el de Fuzzy C-means con un 90,2%. El uso potencial de estas técnicas de clasificación permitirá la detección temprana de enfermedades neurodegenerativas, incluyendo nuevos dispositivos que permitan el análisis de la marcha fuera del laboratorio.
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References
S. Tabrizi, Neurodegenerative diseases neurobiology pathogenesis and therapeutics, vol. 77, no. 2. 2006.
[2] D. R. Peterson and J. D. Bronzino, Biomechanics Principles and Applications, Second. Boca Raton: CRC Press, 2008.
[3] P. M. Thompson and H. V Vinters, “Chapter 1 - Pathologic Lesions in Neurodegenerative Diseases,” in Molecular Biology of Neurodegenerative Diseases, vol. 107, D. B. B. T.-P. in M. B. and T. S. Teplow, Ed. Academic Press, 2012, pp. 1–40.
[4] F. E. Micheli, Enfermedad de Parkinson y trastornos relacionados, 2nd ed. Buenos Aires: Editorial Médica Panamericana s.a., 2006.
[5] S. M. Albert, “Amyotrophic Lateral Sclerosis: A Patient Care Guide for Clinicians,” Neurology, vol. 82, no. 9, pp. 820 LP – 820, Mar. 2014, [Online]. Available: http://n.neurology.org/content/82/9/820.abstract.
[6] V. Medved, Measurement of Human Locomotion. Boca Raton: CRC Press, 2000.
[7] S. Mandeep, S. Mooninder, and S. Paramjeet, “Neuro-Degenerative Disease Diagnosis using Human Gait: A Review,” Int. J. Interact. Multimed. Artif. Intell., vol. 7, no. 1, pp. 16–20, 2013, [Online]. Available: http://www.csjournals.com/IJITKM/PDF 7-1/4.pdf.
[8] R. de M. Roiz, E. W. A. Cacho, M. M. Pazinatto, J. G. Reis, A. Cliquet Jr, and E. M. A. Barasnevicius-Quagliato, “Gait analysis comparing Parkinson’s disease with healthy elderly subjects,” Arq. Neuropsiquiatr., vol. 68, pp. 81–86, 2010, [Online]. Available: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0004-282X2010000100018&nrm=iso.
[9] O. Sofuwa, A. Nieuwboer, K. Desloovere, A.-M. Willems, F. Chavret, and I. Jonkers, “Quantitative Gait Analysis in Parkinson’s Disease: Comparison With a Healthy Control Group,” Arch. Phys. Med. Rehabil., vol. 86, no. 5, pp. 1007–1013, 2005, doi: https://doi.org/10.1016/j.apmr.2004.08.012.
[10] A. Delval et al., “A biomechanical study of gait initiation in Huntington’s disease,” Gait Posture, vol. 25, no. 2, pp. 279–288, 2007, doi: https://doi.org/10.1016/j.gaitpost.2006.04.001.
[11] M. Tinelli, P. Kanavos, and F. Grimaccia, “The value of early diagnosis and treatment in Parkinson’s disease: a literature review of the potential clinical and socioeconomic impact of targeting unmet needs in Parkinson’s disease,” The London School of Economics and Political Science, London, 2016. Accessed: Oct. 17, 2018. [Online]. Available: http://eprints.lse.ac.uk/id/eprint/68479.
[12] S. Bilgin, “The impact of feature extraction for the classification of amyotrophic lateral sclerosis among neurodegenerative diseases and healthy subjects,” Biomed. Signal Process. Control, vol. 31, no. Supplement C, pp. 288–294, 2017, doi: https://doi.org/10.1016/j.bspc.2016.08.016.
[13] Y. Xia, Q. Gao, and Q. Ye, “Classification of gait rhythm signals between patients with neuro-degenerative diseases and normal subjects: Experiments with statistical features and different classification models,” Biomed. Signal Process. Control, vol. 18, no. Complete, pp. 254–262, 2015, doi: 10.1016/j.bspc.2015.02.002.
[14] W. Zeng and C. Wang, “Classification of Neurodegenerative Diseases Using Gait Dynamics via Deterministic Learning,” Inf. Sci., vol. 317, no. C, pp. 246–258, 2015, doi: 10.1016/j.ins.2015.04.047.
[15] R. Martins, C. Figueira, H. Gamboa, A. Veloso, and R. Matias, “Automatising gait kinematics classification with fast and accurate machine learning algorithms,” Gait Posture, vol. 42, p. S101, 2015, doi: https://doi.org/10.1016/j.gaitpost.2015.06.184.
[16] R. Matias, R. Martins, J. Magarreiro, A. L. Gomes, C. Cavaco, and H. Gamboa, “A reliable classification system for neuromusculoskeletal gait disorders,” Gait Posture, vol. 42, p. S78, 2015, doi: https://doi.org/10.1016/j.gaitpost.2015.03.132.
[17] J. Aguilar-Martin and R. Lopez de Mantaras, “The process of classification and learning the meaning of linguistic descriptors of concepts,” in Approximate Reasoning in Decision Analysis, M. M. Gupta and E. Sánchez, Eds. Amsterdam: North-Holland Pub. Co, 1982, pp. 165–175.
[18] J. M. Hausdorff, A. Lertratanakul, M. E. Cudkowicz, A. L. Peterson, D. Kaliton, and A. L. Goldberger, “Gait Dynamics in Neuro-Degenerative Disease Data Base,” 2015. .
[19] J. M. Hausdorff, Z. Ladin, and J. Y. Wei, “Footswitch system for measurement of the temporal parameters of gait,” J. Biomech., vol. 28, no. 3, pp. 347–351, 1995, doi: https://doi.org/10.1016/0021-9290(94)00074-E.
[20] J. M. Hausdorff, A. Lertratanakul, M. E. Cudkowicz, A. L. Peterson, D. Kaliton, and A. L. Goldberger, “Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis,” J. Appl. Physiol., vol. 88, no. 6, pp. 2045–2053, 2000, [Online]. Available: http://jap.physiology.org/content/88/6/2045.
[21] S. Bandyopadhyay and S. Saha, Unsupervised Classification. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
[22] J. C. Dunn, “A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters,” J. Cybern., vol. 3, no. 3, pp. 32–57, Jan. 1973, doi: 10.1080/01969727308546046.
[23] A. C. Rencher, Methods of Multivariate Analysis, 2nd ed. United States of America: Wiley-Interscience, 2002.
[24] S. E. Maxwell, H. D. Delaney, and K. Kelley, Designing Experiments and Analyzing Data: A Model Comparison Perspective, 3rd ed. New York, NY, USA: Routledge, 2017.
[25] K. M. Beavers et al., “Associations between body composition and gait-speed decline: results from the Health, Aging, and Body Composition study,” Am. J. Clin. Nutr., vol. 97, no. 3, pp. 552–560, Mar. 2013, doi: 10.3945/ajcn.112.047860.
[26] J. A. Schrack, E. M. Simonsick, and L. Ferrucci, “The Relationship of the Energetic Cost of Slow Walking and Peak Energy Expenditure to Gait Speed in Mid-to-Late Life,” Am. J. Phys. Med. Rehabil., vol. 92, no. 1, pp. 28–35, Jan. 2013, doi: 10.1097/PHM.0b013e3182644165.
[27] F. Merrikh-Bayat, “Time series analysis of parkinson’s disease, huntington’s disease and amyotrophic lateral sclerosis,” Procedia Comput. Sci., vol. 3, no. Supplement C, pp. 210–215, 2011, doi: https://doi.org/10.1016/j.procs.2010.12.035.
[28] A. Reeve, E. Simcox, and D. Turnbull, “Ageing and Parkinson’s disease: why is advancing age the biggest risk factor?,” Ageing Res. Rev., vol. 14, no. 100, pp. 19–30, Mar. 2014, doi: 10.1016/j.arr.2014.01.004.
[29] A. Eisen, M. Schulzer, M. MacNeil, B. Pant, and E. Mak, “Duration of amyotrophic lateral sclerosis is age dependent.,” Muscle Nerve, vol. 16, no. 1, pp. 27–32, Jan. 1993, doi: 10.1002/mus.880160107.
[2] D. R. Peterson and J. D. Bronzino, Biomechanics Principles and Applications, Second. Boca Raton: CRC Press, 2008.
[3] P. M. Thompson and H. V Vinters, “Chapter 1 - Pathologic Lesions in Neurodegenerative Diseases,” in Molecular Biology of Neurodegenerative Diseases, vol. 107, D. B. B. T.-P. in M. B. and T. S. Teplow, Ed. Academic Press, 2012, pp. 1–40.
[4] F. E. Micheli, Enfermedad de Parkinson y trastornos relacionados, 2nd ed. Buenos Aires: Editorial Médica Panamericana s.a., 2006.
[5] S. M. Albert, “Amyotrophic Lateral Sclerosis: A Patient Care Guide for Clinicians,” Neurology, vol. 82, no. 9, pp. 820 LP – 820, Mar. 2014, [Online]. Available: http://n.neurology.org/content/82/9/820.abstract.
[6] V. Medved, Measurement of Human Locomotion. Boca Raton: CRC Press, 2000.
[7] S. Mandeep, S. Mooninder, and S. Paramjeet, “Neuro-Degenerative Disease Diagnosis using Human Gait: A Review,” Int. J. Interact. Multimed. Artif. Intell., vol. 7, no. 1, pp. 16–20, 2013, [Online]. Available: http://www.csjournals.com/IJITKM/PDF 7-1/4.pdf.
[8] R. de M. Roiz, E. W. A. Cacho, M. M. Pazinatto, J. G. Reis, A. Cliquet Jr, and E. M. A. Barasnevicius-Quagliato, “Gait analysis comparing Parkinson’s disease with healthy elderly subjects,” Arq. Neuropsiquiatr., vol. 68, pp. 81–86, 2010, [Online]. Available: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0004-282X2010000100018&nrm=iso.
[9] O. Sofuwa, A. Nieuwboer, K. Desloovere, A.-M. Willems, F. Chavret, and I. Jonkers, “Quantitative Gait Analysis in Parkinson’s Disease: Comparison With a Healthy Control Group,” Arch. Phys. Med. Rehabil., vol. 86, no. 5, pp. 1007–1013, 2005, doi: https://doi.org/10.1016/j.apmr.2004.08.012.
[10] A. Delval et al., “A biomechanical study of gait initiation in Huntington’s disease,” Gait Posture, vol. 25, no. 2, pp. 279–288, 2007, doi: https://doi.org/10.1016/j.gaitpost.2006.04.001.
[11] M. Tinelli, P. Kanavos, and F. Grimaccia, “The value of early diagnosis and treatment in Parkinson’s disease: a literature review of the potential clinical and socioeconomic impact of targeting unmet needs in Parkinson’s disease,” The London School of Economics and Political Science, London, 2016. Accessed: Oct. 17, 2018. [Online]. Available: http://eprints.lse.ac.uk/id/eprint/68479.
[12] S. Bilgin, “The impact of feature extraction for the classification of amyotrophic lateral sclerosis among neurodegenerative diseases and healthy subjects,” Biomed. Signal Process. Control, vol. 31, no. Supplement C, pp. 288–294, 2017, doi: https://doi.org/10.1016/j.bspc.2016.08.016.
[13] Y. Xia, Q. Gao, and Q. Ye, “Classification of gait rhythm signals between patients with neuro-degenerative diseases and normal subjects: Experiments with statistical features and different classification models,” Biomed. Signal Process. Control, vol. 18, no. Complete, pp. 254–262, 2015, doi: 10.1016/j.bspc.2015.02.002.
[14] W. Zeng and C. Wang, “Classification of Neurodegenerative Diseases Using Gait Dynamics via Deterministic Learning,” Inf. Sci., vol. 317, no. C, pp. 246–258, 2015, doi: 10.1016/j.ins.2015.04.047.
[15] R. Martins, C. Figueira, H. Gamboa, A. Veloso, and R. Matias, “Automatising gait kinematics classification with fast and accurate machine learning algorithms,” Gait Posture, vol. 42, p. S101, 2015, doi: https://doi.org/10.1016/j.gaitpost.2015.06.184.
[16] R. Matias, R. Martins, J. Magarreiro, A. L. Gomes, C. Cavaco, and H. Gamboa, “A reliable classification system for neuromusculoskeletal gait disorders,” Gait Posture, vol. 42, p. S78, 2015, doi: https://doi.org/10.1016/j.gaitpost.2015.03.132.
[17] J. Aguilar-Martin and R. Lopez de Mantaras, “The process of classification and learning the meaning of linguistic descriptors of concepts,” in Approximate Reasoning in Decision Analysis, M. M. Gupta and E. Sánchez, Eds. Amsterdam: North-Holland Pub. Co, 1982, pp. 165–175.
[18] J. M. Hausdorff, A. Lertratanakul, M. E. Cudkowicz, A. L. Peterson, D. Kaliton, and A. L. Goldberger, “Gait Dynamics in Neuro-Degenerative Disease Data Base,” 2015. .
[19] J. M. Hausdorff, Z. Ladin, and J. Y. Wei, “Footswitch system for measurement of the temporal parameters of gait,” J. Biomech., vol. 28, no. 3, pp. 347–351, 1995, doi: https://doi.org/10.1016/0021-9290(94)00074-E.
[20] J. M. Hausdorff, A. Lertratanakul, M. E. Cudkowicz, A. L. Peterson, D. Kaliton, and A. L. Goldberger, “Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis,” J. Appl. Physiol., vol. 88, no. 6, pp. 2045–2053, 2000, [Online]. Available: http://jap.physiology.org/content/88/6/2045.
[21] S. Bandyopadhyay and S. Saha, Unsupervised Classification. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
[22] J. C. Dunn, “A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters,” J. Cybern., vol. 3, no. 3, pp. 32–57, Jan. 1973, doi: 10.1080/01969727308546046.
[23] A. C. Rencher, Methods of Multivariate Analysis, 2nd ed. United States of America: Wiley-Interscience, 2002.
[24] S. E. Maxwell, H. D. Delaney, and K. Kelley, Designing Experiments and Analyzing Data: A Model Comparison Perspective, 3rd ed. New York, NY, USA: Routledge, 2017.
[25] K. M. Beavers et al., “Associations between body composition and gait-speed decline: results from the Health, Aging, and Body Composition study,” Am. J. Clin. Nutr., vol. 97, no. 3, pp. 552–560, Mar. 2013, doi: 10.3945/ajcn.112.047860.
[26] J. A. Schrack, E. M. Simonsick, and L. Ferrucci, “The Relationship of the Energetic Cost of Slow Walking and Peak Energy Expenditure to Gait Speed in Mid-to-Late Life,” Am. J. Phys. Med. Rehabil., vol. 92, no. 1, pp. 28–35, Jan. 2013, doi: 10.1097/PHM.0b013e3182644165.
[27] F. Merrikh-Bayat, “Time series analysis of parkinson’s disease, huntington’s disease and amyotrophic lateral sclerosis,” Procedia Comput. Sci., vol. 3, no. Supplement C, pp. 210–215, 2011, doi: https://doi.org/10.1016/j.procs.2010.12.035.
[28] A. Reeve, E. Simcox, and D. Turnbull, “Ageing and Parkinson’s disease: why is advancing age the biggest risk factor?,” Ageing Res. Rev., vol. 14, no. 100, pp. 19–30, Mar. 2014, doi: 10.1016/j.arr.2014.01.004.
[29] A. Eisen, M. Schulzer, M. MacNeil, B. Pant, and E. Mak, “Duration of amyotrophic lateral sclerosis is age dependent.,” Muscle Nerve, vol. 16, no. 1, pp. 27–32, Jan. 1993, doi: 10.1002/mus.880160107.
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