Detection and Classification of the Schizophrenia with Ocular Artifacts Removal in EEG Signal with Darknet YOLO architecture
Keywords:
EEG, RCNN, YOLO, DARKNET, ClassificationAbstract
Schizophrenia is a devastating mental illness that affects millions of people throughout the world and profoundly alters the way they think, feel, and act. The illness may be accurately diagnosed thanks to the EEG signal. Researchers can capture brain activity non-invasively using an electroencephalogram (EEG). It's a useful technique for diagnosing a wide range of neurological illnesses and diseases of the brain. An electrooculogram, a large electrical potential around the eyes produced by blinking or eye movement, is recorded. When extracortical activity spreads to the scalp, it contaminates EEG data. Ocular artifacts are a term used to describe these Ocular artifacts (OAs). When it comes to EEG analysis, one of the most significant types of interference is the ocular artifact. An important part of the analysis before processing EEG data is OAs removal/reduction. To eliminate the common classical OAs, a multi-channel EEG or a second electrooculogram recording is required. This paper developed a Fejer-Korovkin pre-processing model for OA elimination in the EEG signal. The developed model has termed the FK_DARKNET model with DARKET integrated with the RCNN (Recurrent Convolutional Neural Network) applied over the YOLO architecture. The proposed FK_DARKNET comprises the YOLO model for the classification of Schizophrenia and normal EEG signals in the images. The proposed FK_DARKNET model's efficacy is evaluated in light of the state-of-the-art method. The results of the comparison show that the suggested FK_DARKNET performs better than its competitors in terms of accuracy, TPR, and TNR.
Downloads
References
Gokhan Guney1 et al., “Exploring the attention process differentiation of attention deficit hyperactivity disorder (ADHD) symptomatic adults using artificial intelligence on electroencephalography (EEG) signals,” Turk J Elec Eng & Comp Sci,vol.29,pp. 2312 – 2325,2021.
Mesut Uğurlu et al., “A new classification method for encrypted internet traffic using machine learning,”
Turk J Elec Eng & Comp Sci, vol.29,pp. 2450-2468,2021.
Krishnaveni, V., Jayaraman, S., Kumar, P. M., Shivakumar, K., & Ramadoss, K. (2005). Comparison of independent component analysis algorithms for removal of ocular artifacts from electroencephalogram. Measurement Science Review, 5(2), 67-78.
Jafarifarmand, A., Badamchizadeh, M. A., Khanmohammadi, S., Nazari, M. A., & Tazehkand, B. M. (2017). Real-time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach. Biomedical signal Processing and control, 31, 199-210.
Mashhadi, N., Khuzani, A. Z., Heidari, M., & Khaledyan, D. (2020, September). Deep learning denoising for EOG artifacts removal from EEG signals. In 2020 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 1-6). IEEE.
Turnip, A., Setiawan, I. R., & Junaidi, E. (2014). An experiment of ocular artifacts elimination from EEG signals using ICA and PCA methods. Journal of Mechatronics, Electrical Power, and Vehicular Technology, 5(2), 129-138.
Sarin, M., Verma, A., Mehta, D. H., Shukla, P. K., & Verma, S. (2020, February). Automated ocular artifacts identification and removal from eeg data using hybrid machine learning methods. In 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 1054-1059). IEEE.
Liu, Y., Habibnezhad, M., Jebelli, H., Asadi, S., & Lee, S. (2020, November). Ocular artifacts reduction in EEG signals acquired at construction sites by applying a dependent component analysis (DCA). In Construction Research Congress 2020: Computer Applications (pp. 1281-1289). Reston, VA: American Society of Civil Engineers.
Prasad, D. S., Chanamallu, S. R., & Prasad, K. S. (2021). Mitigation of ocular artifacts for EEG signal using improved earth worm optimization-based neural network and lifting wavelet transform. Computer Methods in Biomechanics and Biomedical Engineering, 24(5), 551-578.
Patel, P., & Satija, U. (2021, July). Performance Analysis of Convolutional Neural Network Based EEG Epileptic Seizure Classification in Presence of Ocular Artifacts. In 2021 National Conference on Communications (NCC) (pp. 1-5). IEEE.
Kokate, P., Pancholi, S., & Joshi, A. M. (2021). Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis. arXiv preprint arXiv:2107.08514.
Jamil, Z., Jamil, A., & Majid, M. (2021). Artifact removal from EEG signals recorded in non-restricted environment. Biocybernetics and Biomedical Engineering, 41(2), 503-515.
Devulapalli, S. P., Chanamallu, S. R., & Kodati, S. P. (2020). A hybrid ICA Kalman predictor algorithm for ocular artifacts removal. International Journal of Speech Technology, 23(4), 727-735.
Shi, Q., Li, Z., Zhang, L., Jiang, H., Tian, F., Zhao, Q., & Hu, B. (2021). High-speed ocular Artifacts Removal of multichannel EEG Based on improved moment matching. Journal of Neural Engineering.
Tosun, M., & Kasım, Ö. (2020). Novel eye-blink artefact detection algorithm from raw EEG signals using FCN-based semantic segmentation method. IET Signal Processing, 14(8), 489-494.
Kim, C. S., Sun, J., Liu, D., Wang, Q., & Paek, S. G. (2017). Removal of ocular artifacts using ICA and adaptive filter for motor imagery-based BCI. IEEE/CAA journal of automatica sinica.
Sun, M., & Peng, H. (2014). Automatic identification and removal of ocular artifacts in EEG—improved adaptive predictor filtering for portable applications. IEEE transactions on nanobioscience, 13(2), 109-117.
Baygin, M., Yaman, O., Tuncer, T., Dogan, S., Barua, P. D., & Acharya, U. R. (2021). Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals. Biomedical Signal Processing and Control, 70, 102936.
Barros, C., Silva, C. A., & Pinheiro, A. P. (2021). Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls. Artificial Intelligence in Medicine, 114, 102039.
Khare, S. K., & Bajaj, V. (2022). A hybrid decision support system for automatic detection of Schizophrenia using EEG signals. Computers in biology and medicine, 141, 105028.
Jindal, K., Upadhyay, R., Padhy, P. K., & Longo, L. (2022). Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals. In Artificial Intelligence-Based Brain-Computer Interface (pp. 145-162). Academic Press.
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.
Sairamya, N. J., Subathra, M. S. P., & George, S. T. (2022). Automatic identification of schizophrenia using EEG signals based on discrete wavelet transform and RLNDiP technique with ANN. Expert Systems with Applications, 192, 116230.
Hwang, S., Jebelli, H., Choi, B., Choi, M., & Lee, S. (2018). “Measuring workers’ emotional state during construction tasks using wearable EEG.” Journal of Construction Engineering and Management, 144(7), 04018050.
Jebelli, H., Khalili, M. M., & Lee, S. (2019). “Mobile EEG-based workers’ stress recognition by applying deep neural network.” Advances in Informatics and Computing in Civil and Construction Engineering (pp. 173-180). Springer, Cham.
Wang, D., Chen, J., Zhao, D., Dai, F., Zheng, C., & Wu, X. (2017). “Monitoring workers’ attention and vigilance in construction activities through a wireless and wearable electroencephalography system.” Automation in Construction, 82, 122-137.
Kakulapati, V., & Jayanthiladevi, A. . (2023). Self-aware COVID-19 AI Approach (SIntL-CoV19) by Integrating Infected Scans with Internal Behavioral Analysis. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 87–93. https://doi.org/10.17762/ijritcc.v11i3.6205
Alexei Ivanov, Machine Learning for Traffic Prediction and Optimization in Smart Cities , Machine Learning Applications Conference Proceedings, Vol 3 2023.
Dhanikonda, S. R., Sowjanya, P., Ramanaiah, M. L., Joshi, R., Krishna Mohan, B. H., Dhabliya, D., & Raja, N. K. (2022). An efficient deep learning model with interrelated tagging prototype with segmentation for telugu optical character recognition. Scientific Programming, 2022 doi:10.1155/2022/1059004
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.