Predição de Congelamento de Marcha por Meio da Detecção de Eventos de Pré-Congelamento
Resumo
Congelamento de marcha (FOG) é um sinal clínico debilitante comumente observado em pacientes com doença de Parkinson. Utilizando dados normalmente capturados por sensores inerciais, a predição automática do FOG pode ajudar a melhorar a qualidade de vida dos pacientes—e.g., evitando quedas. Dentre as estratégias aplicadas, destaca-se o uso de métodos de aprendizagem de máquina para resolver um problema de classificação em três classes: FOG, Não-FOG e Pré-FOG. A classe Pré-FOG refere-se a segmentos de série temporal imediatamente anterior à ocorrência de um evento de FOG. A expectativa é que instâncias de Pré-FOG apresentem características únicas que permitam identificar aspectos de um episódio iminente de FOG. No entanto, essa classe não é bem definida e nem bem caracterizada na literatura devido à sua natureza transicional. Neste artigo nós analisamos o comportamento da classe Pré-FOG usando dois modelos de aprendizagem de máquina, SVM e a combinação de CNN com LSTM, em dados coletados por acelerômetros e giroscópios. Os resultados mostram que Pré-FOG pode ser detectada, mas com taxas de reconhecimento baixas, indicando a falta de padrões discriminantes da classe Pré-FOG em relação às instâncias de FOG ou de Não-FOG.Referências
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Zhao, Y., Tonn, K., Niazmand, K., Fietzek, U. M., D’Angelo, L. T., Ceballos-Baumann, A., and Lueth, T. C. (2012). Online fog identification in parkinson’s disease with a time-frequency combined algorithm. In Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, pages 192–195. IEEE.
Filtjens, B., Ginis, P., Nieuwboer, A., Afzal, M. R., Spildooren, J., Vanrumste, B., and Slaets, P. (2021). Modelling and identification of characteristic kinematic features preceding freezing of gait with convolutional neural networks and layer-wise relevance propagation. BMC medical informatics and decision making, 21(1):1–11.
Hu, K., Wang, Z., Wang, W., Martens, K. A. E., Wang, L., Tan, T., Lewis, S. J., and Feng, D. D. (2019). Graph sequence recurrent neural network for vision-based freezing of gait detection. IEEE Transactions on Image Processing, 29:1890–1901.
Li, B., Yao, Z., Wang, J., Wang, S., Yang, X., and Sun, Y. (2020). Improved deep learning technique to detect freezing of gait in parkinson’s disease based on wearable sensors. Electronics, 9(11):1919.
Mazilu, S., Calatroni, A., Gazit, E., Mirelman, A., Hausdorff, J. M., and Tröster, G. (2015). Prediction of freezing of gait in parkinson’s from physiological wearables: an exploratory study. IEEE journal of biomedical and health informatics, 19(6):1843–1854.
Mazilu, S., Calatroni, A., Gazit, E., Roggen, D., Hausdorff, J. M., and Tröster, G. (2013). Feature learning for detection and prediction of freezing of gait in parkinson’s disease. In Machine Learning and Data Mining in Pattern Recognition: 9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013. Proceedings 9, pages 144–158. Springer.
Naghavi, N., Miller, A., and Wade, E. (2019). Towards real-time prediction of freezing of gait in patients with parkinson’s disease: addressing the class imbalance problem. Sensors, 19(18):3898.
Ordóñez, F. J. and Roggen, D. (2016). Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors, 16(1).
Pardoel, S., Kofman, J., Nantel, J., and Lemaire, E. D. (2019). Wearable-sensor-based detection and prediction of freezing of gait in parkinson’s disease: a review. Sensors, 19(23):5141.
Pardoel, S., Nantel, J., Kofman, J., and Lemaire, E. D. (2022). Prediction of freezing of gait in parkinson’s disease using unilateral and bilateral plantar-pressure data. Frontiers in Neurology, 13.
Pardoel, S., Shalin, G., Nantel, J., Lemaire, E. D., and Kofman, J. (2021). Early detection of freezing of gait during walking using inertial measurement unit and plantar pressure distribution data. Sensors, 21(6):2246.
Polat, K. (2019). Freezing of gait (fog) detection using logistic regression in parkinson’s disease from acceleration signals. In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pages 1–4. Ieee.
Reches, T., Dagan, M., Herman, T., Gazit, E., Gouskova, N. A., Giladi, N., Manor, B., and Hausdorff, J. M. (2020). Using wearable sensors and machine learning to automatically detect freezing of gait during a fog-provoking test. Sensors, 20(16):4474.
Rezvanian, S. and Lockhart, T. E. (2016). Towards real-time detection of freezing of gait using wavelet transform on wireless accelerometer data. Sensors, 16(4):475.
Ribeiro De Souza, C., Miao, R., Ávila De Oliveira, J., Cristina De Lima-Pardini, A., Fragoso De Campos, D., Silva-Batista, C., Teixeira, L., Shokur, S., Mohamed, B., and Coelho, D. B. (2022). A public data set of videos, inertial measurement unit, and clinical scales of freezing of gait in individuals with parkinson’s disease during a turning-in-place task. Frontiers in Neuroscience, 16:832463.
Shalin, G., Pardoel, S., Lemaire, E. D., Nantel, J., and Kofman, J. (2021). Prediction and detection of freezing of gait in parkinson’s disease from plantar pressure data using long short-term memory neural-networks. Journal of neuroengineering and rehabilitation, 18(1):1–15.
Sigcha, L., Costa, N., Pavón, I., Costa, S., Arezes, P., López, J. M., and De Arcas, G. (2020). Deep learning approaches for detecting freezing of gait in parkinson’s disease patients through on-body acceleration sensors. Sensors, 20(7):1895.
Tahafchi, P., Molina, R., Roper, J. A., Sowalsky, K., Hass, C. J., Gunduz, A., Okun, M. S., and Judy, J. W. (2017). Freezing-of-gait detection using temporal, spatial, and physiological features with a support-vector-machine classifier. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 2867–2870. IEEE.
Zhang, W., Huang, D., Li, H., Wang, L., Wei, Y., Pan, K., Ma, L., Feng, H., Pan, J., and Guo, Y. (2021). Sensoring and application of multimodal data for the detection of freezing of gait in parkinson’s disease. arXiv preprint arXiv:2110.04444.
Zhang, Y. and Gu, D. (2019). A deep convolutional-recurrent neural network for freezing of gait detection in patients with parkinson’s disease. In 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pages 1–6. IEEE.
Zhao, Y., Tonn, K., Niazmand, K., Fietzek, U. M., D’Angelo, L. T., Ceballos-Baumann, A., and Lueth, T. C. (2012). Online fog identification in parkinson’s disease with a time-frequency combined algorithm. In Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, pages 192–195. IEEE.
Publicado
27/06/2023
Como Citar
AMOEDO, João Pedro A.; GONÇALVES, Paulo H. N.; ALBUQUERQUE, Rafael; SANTOS, Eulanda M. dos; GIUSTI, Rafael; F. JUNIOR, Renato C..
Predição de Congelamento de Marcha por Meio da Detecção de Eventos de Pré-Congelamento. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 292-303.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2023.229854.