Análise no Desempenho de Algoritmos de Aprendizagem Supervisionada na Classificação da Marcha em Parkinsonianos
Resumo
A análise da marcha tornou-se um mecanismo quantitativo atrativo e não invasivo que pode auxiliar na detecção e monitoramento de portadores da Doença de Parkinson. A extração de características é uma tarefa de suma importância para a qualidade dos dados a serem empregados pelos algoritmos de Aprendizagem de Máquina, visando como principal objetivo a redução na dimensionalidade dos dados em um processo de classificação. Este trabalho avalia o desempenho de algoritmos de aprendizagem supervisionada na classificação das características da marcha humana em portadores de DP.
Referências
Alkhatib, R., Diab, M., Moslem, B., Corbier, C., El Badaoui, M. (2015). Gait-Ground Reaction Force Sensors Selection Based on ROC Curve Evaluation. Journal of Computer and Communications, 3(03), 13.
Chang, D., Alban-Hidalgo, M., Hsu, K. (2014). Diagnosing parkinson’s disease from gait. Stanford.
Daliri, M. R. (2013). Chi-square distance kernel of the gaits for the diagnosis of Parkinson's disease. Biomedical Signal Processing and Control, 8(1), 66-70.
Dasgupta, H. (2015). An algorithm for stance and swing phase detection of human gait cycle. In Electronics and Communication Systems (ICECS), 2015 2nd International Conference, pages 447-450.
Dubey, M., Wadhwani, A. K., Wadhwani, S. (2013). Gait Based Vertical Ground Reaction Force Analysis for Parkinson’s Disease Diagnosis Using Self Organizing Map. International Journal of Advanced Biological and Biomedical Research, 1(6), 624-636.
Faceli, K., Lorena, A. C., Gama, J., Carvalho, A. C. P. L. F. (2011). Inteligência Artificial: Uma abordagem de aprendizado de máquina. Rio de Janeiro: LTC, 2, 192.
Fernandes, Â., Mendes, A., Rocha, N., & Tavares, J. M. R. (2016). Cognitive predictors of balance in Parkinson’s disease. Somatosensory & motor research, 33(2), 67-71.
Geman, O., Ungurean, I., Popa, V., Turcu, C. O. and Găitan, N. C. (2012). Gait in Parkinson's Disease-signal processing and modeling. In 11th International Conference on development and application system, Suceava, Romania.
Guyon, I.; elisseeff, A. (2013). An introduction to variable and feature selection. Journal of machine learning research, 3, pages 1157–1182.
Harrington, P. (2012). Machine learning in action (Vol. 5). Greenwich, CT: Manning.
Kamath, C. (2015). A Novel Approach to Unravel Gait Dynamics Using Symbolic Analysis. Open Access Library Journal, 2(05), 1.
Kim, S. D., Allen, N. E., Canning, C. G., Fung, V. S. (2013). Postural instability in patients with Parkinson’s disease. CNS drugs, 27(2), 97-112.
Lee, S. H., Lim, J. S. (2012). Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction. Expert Systems with Applications, 39(8), 73387344.
Pant, J. K., Krishnan, S. (2014). Foot gait time series estimation based on support vector machine. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pages 6410-6413.
Physionet (2016). “Gait in Parkinson’s Disease”. Homepage disponível em: [link], Março.
Postuma, R. B. (2012). Identifying prodromal parkinson’s disease: Pre-motor disorders in parkinson’s disease. Movement Disorders.Wiley Online Library, 27(5): 617–626.
Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M. and Quattrone, A. (2014). Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy. Journal of Neuroscience Methods, 222, 230-237.
Stegemöller, E. L., Buckley, T. A., Pitsikoulis, C., Barthelemy, E., Roemmich, R., Hass, C. J. (2012). Postural instability and gait impairment during obstacle crossing in Parkinson's disease. Archives of physical medicine and rehabilitation, 93(4), 703-709.
Tan, L. C (2013). Epidemiology of parkinson’s disease. Neurology Asia, 18(3): 231–238.
Willis, A. W. (2010). Geographic and ethnic variation in parkinson disease: a population-based study of us medicare beneficiaries. Neuroepidemiology. Karger Publishers, 34(3): 143–151.
Zaknich, A. (2006). Principles of adaptive filters and self-learning systems. Springer Science & Business Media.
Zhang, Y., Wang, S., Phillips, P., & Ji, G. (2014). Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowledge-Based Systems, 64, 22-31.
Chang, D., Alban-Hidalgo, M., Hsu, K. (2014). Diagnosing parkinson’s disease from gait. Stanford.
Daliri, M. R. (2013). Chi-square distance kernel of the gaits for the diagnosis of Parkinson's disease. Biomedical Signal Processing and Control, 8(1), 66-70.
Dasgupta, H. (2015). An algorithm for stance and swing phase detection of human gait cycle. In Electronics and Communication Systems (ICECS), 2015 2nd International Conference, pages 447-450.
Dubey, M., Wadhwani, A. K., Wadhwani, S. (2013). Gait Based Vertical Ground Reaction Force Analysis for Parkinson’s Disease Diagnosis Using Self Organizing Map. International Journal of Advanced Biological and Biomedical Research, 1(6), 624-636.
Faceli, K., Lorena, A. C., Gama, J., Carvalho, A. C. P. L. F. (2011). Inteligência Artificial: Uma abordagem de aprendizado de máquina. Rio de Janeiro: LTC, 2, 192.
Fernandes, Â., Mendes, A., Rocha, N., & Tavares, J. M. R. (2016). Cognitive predictors of balance in Parkinson’s disease. Somatosensory & motor research, 33(2), 67-71.
Geman, O., Ungurean, I., Popa, V., Turcu, C. O. and Găitan, N. C. (2012). Gait in Parkinson's Disease-signal processing and modeling. In 11th International Conference on development and application system, Suceava, Romania.
Guyon, I.; elisseeff, A. (2013). An introduction to variable and feature selection. Journal of machine learning research, 3, pages 1157–1182.
Harrington, P. (2012). Machine learning in action (Vol. 5). Greenwich, CT: Manning.
Kamath, C. (2015). A Novel Approach to Unravel Gait Dynamics Using Symbolic Analysis. Open Access Library Journal, 2(05), 1.
Kim, S. D., Allen, N. E., Canning, C. G., Fung, V. S. (2013). Postural instability in patients with Parkinson’s disease. CNS drugs, 27(2), 97-112.
Lee, S. H., Lim, J. S. (2012). Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction. Expert Systems with Applications, 39(8), 73387344.
Pant, J. K., Krishnan, S. (2014). Foot gait time series estimation based on support vector machine. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pages 6410-6413.
Physionet (2016). “Gait in Parkinson’s Disease”. Homepage disponível em: [link], Março.
Postuma, R. B. (2012). Identifying prodromal parkinson’s disease: Pre-motor disorders in parkinson’s disease. Movement Disorders.Wiley Online Library, 27(5): 617–626.
Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M. and Quattrone, A. (2014). Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy. Journal of Neuroscience Methods, 222, 230-237.
Stegemöller, E. L., Buckley, T. A., Pitsikoulis, C., Barthelemy, E., Roemmich, R., Hass, C. J. (2012). Postural instability and gait impairment during obstacle crossing in Parkinson's disease. Archives of physical medicine and rehabilitation, 93(4), 703-709.
Tan, L. C (2013). Epidemiology of parkinson’s disease. Neurology Asia, 18(3): 231–238.
Willis, A. W. (2010). Geographic and ethnic variation in parkinson disease: a population-based study of us medicare beneficiaries. Neuroepidemiology. Karger Publishers, 34(3): 143–151.
Zaknich, A. (2006). Principles of adaptive filters and self-learning systems. Springer Science & Business Media.
Zhang, Y., Wang, S., Phillips, P., & Ji, G. (2014). Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowledge-Based Systems, 64, 22-31.
Publicado
02/07/2017
Como Citar
SOUZA, Hugo A.; OLIVEIRA, Marcelo Costa; DE MEDEIROS, Leonardo Melo.
Análise no Desempenho de Algoritmos de Aprendizagem Supervisionada na Classificação da Marcha em Parkinsonianos. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 17. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1859-1868.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2017.3733.
