Modelo Baseado em Transformer para Auxiliar no Diagnóstico de Esclerose Lateral Amiotrófica a partir da Marcha
Resumo
Esclerose Lateral Amiotrófica (ELA) causa, entre outros sintomas, instabilidade da marcha, possui natureza incurável e apresenta um longo e difícil processo de diagnóstico. Por isso, diversos estudos investigam a marcha por meio de modelos de inteligência artificial como uma alternativa para auxiliar no diagnóstico dessas doenças. Este trabalho apresenta um método inovador de detecção de ELA utilizando um modelo baseado em transformer associado a análise de marcha em uma tarefa de classificação binária. Os resultados alcançam valores altos de acurácia e indicam uma alternativa promissora de identificação de ELA.
Palavras-chave:
Esclerose Lateral Amiotrófica, Análise de Marcha, Transformer, Diagnóstico, Inteligência Artificial
Referências
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Chagas, A., Bucci, G., Félix, J., Fonseca, A., Nascimento, H., and Soares, F. (2024). Avaliando a sobreamostragem de dados temporais de marcha no diagnóstico automático de doenças neurodegenerativas. In Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), pages 567–578, Porto Alegre, RS, Brasil. SBC.
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Goldfarb, B. and Simon, S. (1984). Gait patterns in patients with amyotrophic lateral sclerosis. Archives of physical medicine and rehabilitation, 65(2):61—65.
Gupta, K., Khajuria, A., Chatterjee, N., Joshi, P., and Joshi, D. (2019). Rule based classification of neurodegenerative diseases using data driven gait features. Health and Technology, 9(4):547–560.
Hardiman, O., Van Den Berg, L. H., and Kiernan, M. C. (2011). Clinical diagnosis and management of amyotrophic lateral sclerosis. Nature reviews neurology, 7(11):639–649.
Hausdorff, J. M., Ladin, Z., and Wei, J. Y. (1995). Footswitch system for measurement of the temporal parameters of gait. Journal of Biomechanics, 28(3):347–351.
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.
Heemels, M.-T. (2016). Neurodegenerative diseases. Nature, 539(7628):179–180.
Masrori, P. and Van Damme, P. (2020). Amyotrophic lateral sclerosis: a clinical review. European journal of neurology, 27(10):1918–1929.
Mayeux, R. (2003). Epidemiology of neurodegeneration. Annual Review of Neuroscience, 26(1):81–104.
Naimi, S., Bouachir, W., and Bilodeau, G.-A. (2023). 1d-convolutional transformer for parkinson disease diagnosis from gait. Neural Computing and Applications, 36(4):1947–1957.
Nguyen, D. M. D., Miah, M., Bilodeau, G.-A., and Bouachir, W. (2022). Transformers for 1d signals in parkinson’s disease detection from gait. In 2022 26th International Conference on Pattern Recognition (ICPR), pages 5089–5095.
Prabhu, P., Karunakar, A., Anitha, H., and Pradhan, N. (2020). Classification of gait signals into different neurodegenerative diseases using statistical analysis and recurrence quantification analysis. Pattern Recognition Letters, 139:10–16.
Sun, H.-J. and Zhang, Z.-G. (2022). Transformer-based severity detection of parkinson’s symptoms from gait. In 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pages 1–5.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 6000–6010, Red Hook, NY, USA. Curran Associates Inc.
Berman, T. and Bayati, A. (2018). What are Neurodegenerative Diseases and How Do They Affect the Brain? Frontiers for Young Minds, 6.
Beyrami, S. M. G. and Ghaderyan, P. (2020). A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis. Measurement, 156:107579.
Bilgin, S. and Akin, Z. E. (2018). Gait pattern discrimination of als patients using classification methods. Turkish Journal of Electrical Engineering and Computer Sciences, 26(3):1367–1377.
Bucci, G. D. F. F. B., Felix, J., Salvini, R., Nascimento, H., and Soares, F. (2025). Encoder-only transformer for detecting multiple neurodegenerative diseases from gait analysis. In 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), pages 907–912.
Chagas, A., Bucci, G., Félix, J., Fonseca, A., Nascimento, H., and Soares, F. (2024). Avaliando a sobreamostragem de dados temporais de marcha no diagnóstico automático de doenças neurodegenerativas. In Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), pages 567–578, Porto Alegre, RS, Brasil. SBC.
Chagas, A., Bucci, G., Félix, J., Salvini, R., Nascimento, H., and Soares, F. (2025). Exploring biases in machine learning models for neurodegenerative diseases diagnosis through gait and voice analysis. In Anais Estendidos do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, pages 1–6, Porto Alegre, RS, Brasil. SBC.
Elden, R. H., Al-Atabany, W., and Ghoneim, V. F. (2018). Gait variability analysis in neurodegenerative diseases using nonlinear dynamical modelling. In 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), pages 41–44.
Erkkinen, M. G., Kim, M.-O., and Geschwind, M. D. (2018). Clinical neurology and epi demiology of the major neurodegenerative diseases. Cold Spring Harbor Perspectives in Biology, 10(4):a033118.
Goldfarb, B. and Simon, S. (1984). Gait patterns in patients with amyotrophic lateral sclerosis. Archives of physical medicine and rehabilitation, 65(2):61—65.
Gupta, K., Khajuria, A., Chatterjee, N., Joshi, P., and Joshi, D. (2019). Rule based classification of neurodegenerative diseases using data driven gait features. Health and Technology, 9(4):547–560.
Hardiman, O., Van Den Berg, L. H., and Kiernan, M. C. (2011). Clinical diagnosis and management of amyotrophic lateral sclerosis. Nature reviews neurology, 7(11):639–649.
Hausdorff, J. M., Ladin, Z., and Wei, J. Y. (1995). Footswitch system for measurement of the temporal parameters of gait. Journal of Biomechanics, 28(3):347–351.
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.
Heemels, M.-T. (2016). Neurodegenerative diseases. Nature, 539(7628):179–180.
Masrori, P. and Van Damme, P. (2020). Amyotrophic lateral sclerosis: a clinical review. European journal of neurology, 27(10):1918–1929.
Mayeux, R. (2003). Epidemiology of neurodegeneration. Annual Review of Neuroscience, 26(1):81–104.
Naimi, S., Bouachir, W., and Bilodeau, G.-A. (2023). 1d-convolutional transformer for parkinson disease diagnosis from gait. Neural Computing and Applications, 36(4):1947–1957.
Nguyen, D. M. D., Miah, M., Bilodeau, G.-A., and Bouachir, W. (2022). Transformers for 1d signals in parkinson’s disease detection from gait. In 2022 26th International Conference on Pattern Recognition (ICPR), pages 5089–5095.
Prabhu, P., Karunakar, A., Anitha, H., and Pradhan, N. (2020). Classification of gait signals into different neurodegenerative diseases using statistical analysis and recurrence quantification analysis. Pattern Recognition Letters, 139:10–16.
Sun, H.-J. and Zhang, Z.-G. (2022). Transformer-based severity detection of parkinson’s symptoms from gait. In 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pages 1–5.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 6000–6010, Red Hook, NY, USA. Curran Associates Inc.
Publicado
04/12/2025
Como Citar
BUCCI, Giordana de Farias F. B.; CHAGAS, Ana Luísa de Bastos; LOBO, Pedro L. S.; NASCIMENTO, Hugo A. D. do; SOARES, Fabrizzio; FELIX, Juliana.
Modelo Baseado em Transformer para Auxiliar no Diagnóstico de Esclerose Lateral Amiotrófica a partir da Marcha. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 13. , 2025, Luziânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 215-224.
DOI: https://doi.org/10.5753/erigo.2025.17124.
