Utilização de Redes Neurais Convolucionais para Classificação da Esquizofrenia através de Microestados
Resumo
Este artigo explora a aplicação de redes neurais convolucionais (CNNs) na classificação da esquizofrenia através de microestados gerados do eletroencefalograma (EEG). A base de dados é balanceada, consistindo em 28 pacientes divididos igualmente entre indivíduos com esquizofrenia e saudáveis. Os microestados, gerados através do Global Field Power (GFP), foram utilizados como entrada para uma CNN com quatro camadas convolucionais e três camadas totalmente conectadas. Os resultados são promissores, o modelo atingiu uma acurácia de 75%, sensibilidade de 71,4%, precisão de 76,9% e medida-F1 de 74,1%.Referências
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Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. (2021). CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics, 10(20):2470.
Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for multi-class classification: an overview. ArXiv, abs/2008.05756.
Hany, M., Rehman, B., Rizvi, A., et al. (2024). Schizophrenia. StatPearls Publishing, Treasure Island (FL). Updated 2024 Feb 23.
Karlsgodt, K. H., Sun, D., and Cannon, T. D. (2010). Structural and functional brain abnormalities in schizophrenia. Current directions in psychological science, 19(4):226–231.
Keihani, A., Sajadi, S. S., Hasani, M., and Ferrarelli, F. (2022). Bayesian optimization of machine learning classification of resting-state EEG microstates in schizophrenia: a proof-of-concept preliminary study based on secondary analysis. Brain Sciences, 12(11):1497.
Khanna, A., Pascual-Leone, A., Michel, C. M., and Farzan, F. (2015). Microstates in resting-state eeg: current status and future directions. Neuroscience & Biobehavioral Reviews, 49:105–113.
Kim, K., Duc, N. T., Choi, M., and Lee, B. (2021). EEG microstate features for schizophrenia classification. PloS one, 16(5):e0251842.
Lun, X., Yu, Z., Chen, T., Wang, F., and Hou, Y. (2020). A simplified CNN classification method for MI-EEG via the electrode pairs signals. Frontiers in Human Neuroscience, 14.
Michel, C. M. and Koenig, T. (2018). EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. NeuroImage, 180:577–593. Brain Connectivity Dynamics.
Olejarczyk, E. and Jernajczyk, W. (2017). EEG in schizophrenia. DOI: 10.18150/repod.0107441.
Patel, K. R., Cherian, J., Gohil, K., and Atkinson, D. (2014). Schizophrenia: overview and treatment options. Pharmacy and Therapeutics, 39(9):638.
Skrandies, W. (1990). Global field power and topographic similarity. Brain topography, 3:137–141.
Sun, Q., Zhou, J., Guo, H., Gou, N., Lin, R., Huang, Y., Guo, W., and Wang, X. (2021). EEG microstates and its relationship with clinical symptoms in patients with schizophrenia. Frontiers in Psychiatry, 12:761203.
Tudor, M., Tudor, L., and Tudor, K. I. (2005). Hans Berger (1873-1941) – the history of electroencephalography. Acta medica Croatica: casopis Hravatske akademije medicinskih znanosti, 59(4):307–313.
Wang, Y., Pan, Y., and Li, H. (2020). What is brain health and why is it important? BMJ, 371(m3683).
Yamashita, R., Nishio, M., Do, R. K. G., and Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9:611–629.
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., and Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57(4):99.
Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. (2021). CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics, 10(20):2470.
Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for multi-class classification: an overview. ArXiv, abs/2008.05756.
Hany, M., Rehman, B., Rizvi, A., et al. (2024). Schizophrenia. StatPearls Publishing, Treasure Island (FL). Updated 2024 Feb 23.
Karlsgodt, K. H., Sun, D., and Cannon, T. D. (2010). Structural and functional brain abnormalities in schizophrenia. Current directions in psychological science, 19(4):226–231.
Keihani, A., Sajadi, S. S., Hasani, M., and Ferrarelli, F. (2022). Bayesian optimization of machine learning classification of resting-state EEG microstates in schizophrenia: a proof-of-concept preliminary study based on secondary analysis. Brain Sciences, 12(11):1497.
Khanna, A., Pascual-Leone, A., Michel, C. M., and Farzan, F. (2015). Microstates in resting-state eeg: current status and future directions. Neuroscience & Biobehavioral Reviews, 49:105–113.
Kim, K., Duc, N. T., Choi, M., and Lee, B. (2021). EEG microstate features for schizophrenia classification. PloS one, 16(5):e0251842.
Lun, X., Yu, Z., Chen, T., Wang, F., and Hou, Y. (2020). A simplified CNN classification method for MI-EEG via the electrode pairs signals. Frontiers in Human Neuroscience, 14.
Michel, C. M. and Koenig, T. (2018). EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. NeuroImage, 180:577–593. Brain Connectivity Dynamics.
Olejarczyk, E. and Jernajczyk, W. (2017). EEG in schizophrenia. DOI: 10.18150/repod.0107441.
Patel, K. R., Cherian, J., Gohil, K., and Atkinson, D. (2014). Schizophrenia: overview and treatment options. Pharmacy and Therapeutics, 39(9):638.
Skrandies, W. (1990). Global field power and topographic similarity. Brain topography, 3:137–141.
Sun, Q., Zhou, J., Guo, H., Gou, N., Lin, R., Huang, Y., Guo, W., and Wang, X. (2021). EEG microstates and its relationship with clinical symptoms in patients with schizophrenia. Frontiers in Psychiatry, 12:761203.
Tudor, M., Tudor, L., and Tudor, K. I. (2005). Hans Berger (1873-1941) – the history of electroencephalography. Acta medica Croatica: casopis Hravatske akademije medicinskih znanosti, 59(4):307–313.
Wang, Y., Pan, Y., and Li, H. (2020). What is brain health and why is it important? BMJ, 371(m3683).
Yamashita, R., Nishio, M., Do, R. K. G., and Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9:611–629.
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., and Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57(4):99.
Publicado
17/10/2024
Como Citar
VIANNA, João Vitor M.; KOMATI, Karin Satie.
Utilização de Redes Neurais Convolucionais para Classificação da Esquizofrenia através de Microestados. In: ESCOLA REGIONAL DE INFORMÁTICA DO ESPÍRITO SANTO (ERI-ES), 9. , 2024, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 81-90.
DOI: https://doi.org/10.5753/eries.2024.244555.
