Transformer: The Power of Attention to Support the Diagnosis of Multiple Neurodegenerative Diseases
DOI:
https://doi.org/10.5753/reic.2025.6048Keywords:
Neurodegenerative diseases, diagnosis, transformer, gaitAbstract
Neurodegenerative diseases (NDDs) cause, among other symptoms, gait instability, have an incurable nature, and present a long and challenging diagnostic process. For this reason, several studies have investigated gait using artificial intelligence models as an alternative to assist in the diagnosis of these diseases. This study presents the main results obtained during an undergraduate research project using an innovative method for detecting NDDs, using an Encoder-Only Transformer combined with gait analysis in a multi-class classification task. The results achieve high accuracy values and indicate a promising alternative for NDD identification.
Downloads
References
Agarwal, K., Dheekollu, L., Dhama, G., Arora, A., Asthana, S., and Bhowmik, T. (2020). Deep learning based time series forecasting. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 859–864. DOI: 10.1109/ICMLA51294.2020.00140.
Amooei, E., Sharifi, A., and Manthouri, M. (2023). Early diagnosis of neurodegenerative diseases using cnn-lstm and wavelet transform. Journal of Healthcare Informatics Research, 7(1):104–124. DOI: 10.1007/s41666-023-00130-9.
Bucci, G. d. F. F. B., Chagas, A. L., Felix, J., and Nascimento, H. (2024). Aprendizado de máquina profundo aplicado ao diagnóstico de doenças neurodegenerativas a partir da marcha. In 21º Congresso de Pesquisa, Ensino e Extensão (CONPEEX), Goiânia, GO, Brasil, Novembro 06–08, 2024, page 385. UFG. Disponível em: [link].
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), Toronto, Canada, pages 1–5. DOI: 10.1109/COMPSAC65507.2025.00119.
Chagas, A., Bucci, G., Félix, J., Fonseca, A., Nascimento, H., and Soares, F. (2024a). 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. DOI: 10.5753/sbcas.2024.2776.
Chagas, A. L. d. B., Felix, J., Bucci, G., and Nascimento, H. (2024b). Diagnóstico de doenças neurodegenerativas a partir da marcha: Uma aplicação de métodos clássicos de aprendizado de máquina. In 21º Congresso de Pesquisa, Ensino e Extensão (CONPEEX), Goiânia, GO, Brasil, Novembro 06–08, 2024, page 1023. UFG. Disponível em: [link].
Erdaş, Ç. B. and Sümer, E. (2024). CNN-based neurodegenerative disease classification using QR-represented gait data. Brain and Behavior, 14(10). DOI: 10.1002/brb3.70100.
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. DOI: 10.1016/j.bspc.2021.103069.
Erdaş, Ç. B., Sümer, E., and Kibaroğlu, S. (2023). Neurodegenerative diseases detection and grading using gait dynamics. Multimedia Tools and Applications, 82(15):22925–22942. DOI: 10.1007/s11042-023-14461-7.
Erkkinen, M. G., Kim, M.-O., and Geschwind, M. D. (2018). Clinical neurology and epidemiology of the major neurodegenerative diseases. Cold Spring Harbor Perspectives in Biology, 10(4):a033118. DOI: 10.1101/cshperspect.a033118.
Faisal, M. A. A., Chowdhury, M. E. H., Mahbub, Z. B., Pedersen, S., Ahmed, M. U., Khandakar, A., Alhatou, M., Nabil, M., Ara, I., Bhuiyan, E. H., Mahmud, S., and AbdulMoniem, M. (2023). NDDNet: A deep learning model for predicting neurodegenerative diseases from gait pattern. Applied Intelligence, 53(17):20034–20046. DOI: 10.1007/s10489-023-04557-w.
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. DOI: 10.1371/journal.pone.0252380.
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. DOI: 10.1016/0021-9290(94)00074-e.
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. DOI: 10.1152/jappl.2000.88.6.2045.
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. DOI: 10.1152/jappl.1997.82.1.262.
Heemels, M.-T. (2016). Neurodegenerative diseases. Nature, 539(7628):179–180. DOI: 10.1038/539179a.
Maragakis, N. J. and Rothstein, J. D. (2006). Mechanisms of disease: astrocytes in neurodegenerative disease. Nature Clinical Practice Neurology, 2(12):679–689. DOI: 10.1038/ncpneuro0355.
Mayeux, R. (2003). Epidemiology of neurodegeneration. Annual Review of Neuroscience, 26(1):81–104. DOI: 10.1146/annurev.neuro.26.043002.094919.
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. DOI: 10.1007/s00521-023-09193-6.
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. DOI: 10.1109/ICPR56361.2022.9956330.
Ning, Z., Li, L., and Jin, X. (2018). Classification of neuro-degenerative diseases based on CNN and LSTM. In 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pages 82–85. IEEE. DOI: 10.1109/ITME.2018.00029.
Rao, H., Zeng, M., Zhao, X., and Miao, C. (2025). A survey of artificial intelligence in gait-based neurodegenerative disease diagnosis. Neurocomputing, 626:129533. DOI: 10.1016/j.neucom.2025.129533.
Silva, M. I. d., Felix, J. P., Prado, T. d. S., Chagas, A. L. d. B., Bucci, G. d. F. F. B., da Fonseca, A. U., and Soares, F. (2024). Sobre a análise de sinais de voz para o diagnóstico da doença de Parkinson. Journal of Health Informatics, 16(Especial). DOI: 10.59681/2175-4411.v16.iEspecial.2024.1305.
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. DOI: 10.1109/CISP-BMEI56279.2022.9980289.
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. Disponível em: [link].
Zhao, A., Qi, L., Li, J., Dong, J., and Yu, H. (2018). LSTM for diagnosis of neurodegenerative diseases using gait data. In Yu, H. and Dong, J., editors, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), volume 10615, page 106155B. International Society for Optics and Photonics, SPIE. DOI: 10.1117/12.2305277.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 The authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
