1
Universidad Regional Autonoma de los Andes (UNIANDES Ambato), Ecuador
(ua.elizabethmayorga@uniandes.edu.ec)
2
Universidad Regional Autonoma de los Andes (UNIANDES Santo Domingo), Ecuador
(us.robertoab26@uniandes.edu.ec)
3
Universidad Regional Autonoma de los Andes (UNIANDES), Ecuador
(ua.neydahernandez@uniandes.edu.ec)
Abstract :
Schizophrenia, a complex psychiatric disorder, presents a significant challenge in early diagnosis and intervention. In this study, we introduce an intelligent approach to schizophrenia detection based on the fusion of multivariate electroencephalography (EEG) signals. Our methodology encompasses the integration of EEG data from multiple electrodes into multivariate input segments, which are then passed into a LightGBM (Light Gradient Boosting Machine) classification model. We systematically explore the fusion process, leveraging the spatiotemporal information captured by EEG signals, and employ machine learning to discern subtle patterns indicative of schizophrenia. To evaluate the effectiveness of our approach, we compare our model against state-of-the-art machine learning algorithms. Our results demonstrate that our LightGBM-based model outperforms existing methods, achieving competitive performance in the accurate identification of individuals with schizophrenia.
Keywords :
Schizophrenia Diagnosis; Electroencephalography Fusion; Multivariate EEG Analysis; EEG Data Fusion; Fusion of Brain Signals; Deep Learning
References :
[1] Hassan, F., Hussain, S. F., & Qaisar, S. M. (2023). Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques. Information Fusion, 92, 466-478.
[2] Nehal Mostafa, Ibrahim El-henawy, Ahmed Sleem, Fusion of Machine learning for Detection of Rumor and False Information in Social Network, Fusion: Practice and Applications, Vol. 4 , No. 1 , (2021) : 41-57 (Doi : https://doi.org/10.54216/FPA.040105)
[3] Baygin, M., Barua, P. D., Chakraborty, S., Tuncer, I., Dogan, S., Palmer, E., ... & Acharya, U. R. (2023). CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals. Physiological Measurement, 44(3), 035008.
[4] Kumar, A., Sharma, K., & Sharma, A. (2021). Hierarchical deep neural network for mental stress state detection using IoT based biomarkers. Pattern Recognition Letters, 145, 81-87.
[5] A. M.Ali and A. Abdelhafeez, “DeepHAR-Net: A Novel Machine Intelligence Approach for Human Activity Recognition from Inertial Sensors”, SMIJ, vol. 1, Nov. 2022.
[6] Akbari, H., Ghofrani, S., Zakalvand, P., & Sadiq, M. T. (2021). Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features. Biomedical Signal Processing and Control, 69, 102917.
[7] Lin, Y., Ling, B. W. K., Wang, W., Hu, L., Xu, N., & Zhou, X. (2023). Fusion of electroencephalograms at different channels and different activities via multivariate quaternion valued singular spectrum analysis for intellectual and developmental disorder recognition. Biomedical Signal Processing and Control, 79, 104256.
[8] Grover, N., Chharia, A., Upadhyay, R., & Longo, L. (2023). Schizo-Net: A novel Schizophrenia Diagnosis framework using late fusion multimodal deep learning on Electroencephalogram-based Brain connectivity indices. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 464-473.
[9] Xiong, Y., Li, J., Wu, D., Dong, F., Liu, J., Jiang, L., ... & Xu, Y. (2023). Seizure detection algorithm based on fusion of spatio-temporal network constructed with dispersion index. Biomedical Signal Processing and Control, 79, 104155.
[10] Sadeghi, D., Shoeibi, A., Ghassemi, N., Moridian, P., Khadem, A., Alizadehsani, R., ... & Acharya, U. R. (2022). An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Computers in Biology and Medicine, 146, 105554.
[11] Correa, N. M., Li, Y. O., Adali, T., & Calhoun, V. D. (2008). Canonical correlation analysis for feature-based fusion of biomedical imaging modalities and its application to detection of associative networks in schizophrenia. IEEE journal of selected topics in signal processing, 2(6), 998-1007.
[12] Das, K., & Pachori, R. B. (2022). Electroencephalogram based motor imagery brain computer interface using multivariate iterative filtering and spatial filtering. IEEE Transactions on Cognitive and Developmental Systems.
[13] Khare, S. K., Bajaj, V., & Acharya, U. R. (2023). SchizoNET: a robust and accurate Margenau–Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals. Physiological Measurement, 44(3), 035005.
[14] WeiKoh, J. E., Rajinikanth, V., Vicnesh, J., Pham, T. H., Oh, S. L., Yeong, C. H., ... & Cheong, K. H. (2022). Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals. Expert Systems, e12957.
[15] Tahura, S., Hasnat Samiul, S. M., Shamim Kaiser, M., & Mahmud, M. (2021). Anomaly detection in electroencephalography signal using deep learning model. In Proceedings of International Conference on Trends in Computational and Cognitive Engineering: Proceedings of TCCE 2020 (pp. 205-217). Springer Singapore.
[16] Khare, S. K., Bajaj, V., Gaikwad, N. B., & Sinha, G. R. (2023). Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals. Sensors, 23(18), 7860.
[17] Tasci, I., Tasci, B., Barua, P. D., Dogan, S., Tuncer, T., Palmer, E. E., ... & Acharya, U. R. (2023). Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals. Information Fusion, 96, 252-268.
[18] Li, X., & Huang, H. (2023). An IoT-based Intelligent Selection of Multi-domain Feature for Smart Healthcare using reinforcement learning in Schizophrenia. IEEE Internet of Things Journal.
[19] Karnati, M., Sahu, G., Gupta, A., Seal, A., & Krejcar, O. (2023). A Pyramidal Spatial-based Feature Attention Network for Schizophrenia Detection using Electroencephalography Signals. IEEE Transactions on Cognitive and Developmental Systems.
[20] Goshvarpour, A., & Goshvarpour, A. (2022). Schizophrenia diagnosis by weighting the entropy measures of the selected EEG channel. Journal of Medical and Biological Engineering, 42(6), 898-908.
| Style | # |
|---|---|
| MLA | Elizabeth Mayorga Aldaz, Roberto Aguilar Berrezueta, Neyda Hernandez Bandera. "An Intelligent Schizophrenia Detection based on the Fusion of Multivariate Electroencephalography Signals." Fusion: Practice and Applications, Vol. 13, No. 2, 2023 ,PP. 42-51 (Doi : https://doi.org/10.54216/FPA.130204) |
| APA | Elizabeth Mayorga Aldaz, Roberto Aguilar Berrezueta, Neyda Hernandez Bandera. (2023). An Intelligent Schizophrenia Detection based on the Fusion of Multivariate Electroencephalography Signals. Journal of Fusion: Practice and Applications, 13 ( 2 ), 42-51 (Doi : https://doi.org/10.54216/FPA.130204) |
| Chicago | Elizabeth Mayorga Aldaz, Roberto Aguilar Berrezueta, Neyda Hernandez Bandera. "An Intelligent Schizophrenia Detection based on the Fusion of Multivariate Electroencephalography Signals." Journal of Fusion: Practice and Applications, 13 no. 2 (2023): 42-51 (Doi : https://doi.org/10.54216/FPA.130204) |
| Harvard | Elizabeth Mayorga Aldaz, Roberto Aguilar Berrezueta, Neyda Hernandez Bandera. (2023). An Intelligent Schizophrenia Detection based on the Fusion of Multivariate Electroencephalography Signals. Journal of Fusion: Practice and Applications, 13 ( 2 ), 42-51 (Doi : https://doi.org/10.54216/FPA.130204) |
| Vancouver | Elizabeth Mayorga Aldaz, Roberto Aguilar Berrezueta, Neyda Hernandez Bandera. An Intelligent Schizophrenia Detection based on the Fusion of Multivariate Electroencephalography Signals. Journal of Fusion: Practice and Applications, (2023); 13 ( 2 ): 42-51 (Doi : https://doi.org/10.54216/FPA.130204) |
| IEEE | Elizabeth Mayorga Aldaz, Roberto Aguilar Berrezueta, Neyda Hernandez Bandera, An Intelligent Schizophrenia Detection based on the Fusion of Multivariate Electroencephalography Signals, Journal of Fusion: Practice and Applications, Vol. 13 , No. 2 , (2023) : 42-51 (Doi : https://doi.org/10.54216/FPA.130204) |