Avaliando a Sobreamostragem de Dados Temporais de Marcha no Diagnóstico Automático de Doenças Neurodegenerativas
Resumo
Doenças neurodegenerativas (DNDs) causam, dentre outros sintomas, o comprometimento da marcha. Diversos estudos analisam a marcha para, com o auxílio da inteligência artificial, auxiliar no diagnóstico de DNDs. Devido à dificuldade de coleta de novos dados, a técnica de sobreamostragem através do janelamento de dados é frequentemente utilizada. No entanto, um estudo anterior apontou para um possível enviesamento na fase de treinamento de algoritmos de classificação utilizando técnicas de janelamento. Este trabalho investiga esse viés, avaliando, além da validação cruzada tradicionalmente utilizada, uma segunda abordagem, em que tratamos o enviesamento apontado. Os resultados indicam a necessidade de cuidados extras quando se lida com sobreamostragem de dados temporais de marcha.Referências
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Duda, R. O., Hart, P. E., and Stork, D. G. (2000). Pattern Classification (2nd Edition). Wiley-Interscience.
Dutta, S., Chatterjee, A., and Munshi, S. (2009). An automated hierarchical gait pattern identification tool employing cross-correlation-based feature extraction and recurrent neural network based classification. Expert Systems, 26(2):202–217.
Erdaş, Ç. B., Sümer, E., and Kibaroğlu, S. (2021). Neurodegenerative disease detection and severity prediction using deep learning approaches. Biomedical Signal Processing and Control, 70:103069.
Felix, J., Fonseca, A. U., Nascimento, H., and Guimarães, N. (2022). Rede Neural Multicamadas para Classificação de Doenças Neurodegenerativas a partir de Sinais de Marcha. In Anais do XXIV Congresso Brasileiro de Automática, pages 1354–1361. SBA.
Felix, J. P., do Nascimento, H. A. D., Guimarães, N. N., Pires, E. D. O., da Silva Vieira, G., and de Souza Alencar, W. (2020). An Effective and Automatic Method to Aid the Diagnosis of Amyotrophic Lateral Sclerosis Using One Minute of Gait Signal. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 2745–2751. IEEE.
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Felix, J. P., Vieira, F. H. T., da Silva Vieira, G., Franco, R. A. P., da Costa, R. M., and Salvini, R. L. (2019). An Automatic Method for Identifying Huntington’s Disease using Gait Dynamics. In 31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019, Portland, OR, USA, November 4-6, 2019, pages 1659–1663. IEEE.
Fraiwan, L. and Hassanin, O. (2021). Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers. Plos one, 16(6):e0252380.
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Hausdorff, J. M., Mitchell, S. L., Firtion, R., Peng, C.-K., Cudkowicz, M. E., Wei, J. Y., and Goldberger, A. L. (1997). Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. Journal of Applied Physiology, 82(1):262–269.
Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., Xue, Z., Palaniappan, K., Singh, R. K., Antani, S., et al. (2013). Automatic tuberculosis screening using chest radiographs. IEEE transactions on medical imaging, 33(2):233–245.
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. (2020). Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology.
Mayeux, R. (2003). Epidemiology of neurodegeneration. Annual Review of Neuroscience, 26(1):81–104.
Ning, Z., Li, L., and Jin, X. (2018). Classification of neurodegenerative diseases based on CNN and LSTM. In 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pages 82–85. IEEE.
Rajput, D., Wang, W.-J., and Chen, C.-C. (2023). Evaluation of a decided sample size in machine learning applications. BMC Bioinformatics, 24.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3):379–423.
Bohr, A. and Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare, pages 25–60. Elsevier.
Bujang, M. A. and Adnan, T. H. (2016). Requirements for minimum sample size for sensitivity and specificity analysis. Journal of Clinical and Diagnostic Research : JCDR, 10:YE01–YE06.
Çallı, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K. G., and Murphy, K. (2021). Deep learning for chest x-ray analysis: A survey. Medical Image Analysis, 72:102125.
Cicirelli, G., Impedovo, D., Dentamaro, V., Marani, R., Pirlo, G., and D’Orazio, T. (2021). Human gait analysis in neurodegenerative diseases: A review. IEEE Journal of Biomedical and Health Informatics, PP:1–1.
Duda, R. O., Hart, P. E., and Stork, D. G. (2000). Pattern Classification (2nd Edition). Wiley-Interscience.
Dutta, S., Chatterjee, A., and Munshi, S. (2009). An automated hierarchical gait pattern identification tool employing cross-correlation-based feature extraction and recurrent neural network based classification. Expert Systems, 26(2):202–217.
Erdaş, Ç. B., Sümer, E., and Kibaroğlu, S. (2021). Neurodegenerative disease detection and severity prediction using deep learning approaches. Biomedical Signal Processing and Control, 70:103069.
Felix, J., Fonseca, A. U., Nascimento, H., and Guimarães, N. (2022). Rede Neural Multicamadas para Classificação de Doenças Neurodegenerativas a partir de Sinais de Marcha. In Anais do XXIV Congresso Brasileiro de Automática, pages 1354–1361. SBA.
Felix, J. P., do Nascimento, H. A. D., Guimarães, N. N., Pires, E. D. O., da Silva Vieira, G., and de Souza Alencar, W. (2020). An Effective and Automatic Method to Aid the Diagnosis of Amyotrophic Lateral Sclerosis Using One Minute of Gait Signal. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 2745–2751. IEEE.
Felix, J. P., Nascimento, H. A. D. d., Guimarães, N. N., Pires, E. D. O., Da Fonseca, A. U., and Vieira, G. D. S. (2021). Automatic Classification of Amyotrophic Lateral Sclerosis through Gait Dynamics. In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pages 1942–1947. IEEE.
Felix, J. P., Vieira, F. H. T., da Silva Vieira, G., Franco, R. A. P., da Costa, R. M., and Salvini, R. L. (2019). An Automatic Method for Identifying Huntington’s Disease using Gait Dynamics. In 31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019, Portland, OR, USA, November 4-6, 2019, pages 1659–1663. IEEE.
Fraiwan, L. and Hassanin, O. (2021). Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers. Plos one, 16(6):e0252380.
Ghumbre, S. U. and Ghatol, A. A. (2012). Heart disease diagnosis using machine learning algorithm. In Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012, pages 217–225. Springer.
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000 (June 13)). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215–e220. Circulation Electronic Pages: [link] PMID:1085218; DOI: 10.1161/01.CIR.101.23.e215.
Hausdorff, J. (2000). Gait in neurodegenerative disease database. [link]. Acessado por último em 07 de março de 2024.
Hausdorff, J. M., Lertratanakul, A., Cudkowicz, M. E., Peterson, A. L., Kaliton, D., and Goldberger, A. L. (2000). Dynamic Markers of Altered Gait Rhythm in Amyotrophic Lateral Sclerosis. Journal of Applied Physiology, 88(6):2045–2053.
Hausdorff, J. M., Mitchell, S. L., Firtion, R., Peng, C.-K., Cudkowicz, M. E., Wei, J. Y., and Goldberger, A. L. (1997). Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. Journal of Applied Physiology, 82(1):262–269.
Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., Xue, Z., Palaniappan, K., Singh, R. K., Antani, S., et al. (2013). Automatic tuberculosis screening using chest radiographs. IEEE transactions on medical imaging, 33(2):233–245.
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. (2020). Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology.
Mayeux, R. (2003). Epidemiology of neurodegeneration. Annual Review of Neuroscience, 26(1):81–104.
Ning, Z., Li, L., and Jin, X. (2018). Classification of neurodegenerative diseases based on CNN and LSTM. In 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pages 82–85. IEEE.
Rajput, D., Wang, W.-J., and Chen, C.-C. (2023). Evaluation of a decided sample size in machine learning applications. BMC Bioinformatics, 24.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3):379–423.
Publicado
25/06/2024
Como Citar
CHAGAS, Ana Luísa de Bastos; BUCCI, Giordana de Farias F. B.; FÉLIX, Juliana Paula; FONSECA, Afonso Ueslei da; NASCIMENTO, Hugo A. D. do; SOARES, Fabrizzio.
Avaliando a Sobreamostragem de Dados Temporais de Marcha no Diagnóstico Automático de Doenças Neurodegenerativas. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 567-578.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2024.2776.
