EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence
Abstract Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system “EpilNet” is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals.
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Abbasi, M. U., Rashad, A., Basalamah, A., and Tariq, M. «Detection of Epilepsy Seizures in Neo-Natal EEG Using LSTM Architecture», IEEE Access, 7, pp 179074-179085, 2019.
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Cruz, N. E., Solarte, J. and Gonzalez-Vargas, A. «Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy», In Workshop on Engineering Applications, October 2018, pp. 204-213.
Džaferovi?, E., Vrtagi?, S., Bandi?, L., Kevric, J., Subasi, A. and Qaisar, S. M. «Cloud-based mobile platform for EEG signal analysis», In 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), December 2016, pp. 1-4.
Kolb, R., Pineda, K., Curran, M., Skinner, T., Adegbege, A. and Busha, B. F. «Ambulatory Epileptic Monitoring Device», In 40th Annual Northeast Bioengineering Conference (NEBEC), April 2014, pp. 1-2.
Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L. and Ge, Z. «Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study», In Proceedings of the Australasian Computer Science Week Multiconference, February 2020, pp. 1-8.
Mao, W. L., Fathurrahman, H.I.K.,Lee, Y. and Chang, T. W. «EEG dataset classification using CNN method», In Journal of Physics: Conference Series, Vol. 1456, No. 1, January 2020, pp. 012017.
Marzuki, N. A., Husain, W. and Shahiri, A. M. «MyEpiPal: Mobile Application for Managing, Monitoring and Predicting Epilepsy Patient», In International Conference on Advances in Information and Communication Technology, 2016 December, pp. 383-392.
Menshawy, M. E., Benharref, A. and Serhani, M. «An automatic mobile-health based approach for EEG epileptic seizures detection», Expert systems with applications, 42(20), pp 7157-7174, 2015.
Myers, M. H., Threatt, M., Solies, K. M., McFerrin, B. M., Hopf, L. B., Birdwell, J. D. and Sillay, K. A. «Ambulatory seizure monitoring: From concept to prototype device», Annals of neurosciences, 23(2), pp 100-111, 2016.
Pinho, F., Cerqueira, J., Correia, J., Sousa, N. and Dias, N. «myBrain: a novel EEG embedded system for epilepsy monitoring», Journal of medical engineering & technology, 41(7), pp 564-585, 2017.
Ranganathan, L. N., Chinnadurai, S. A., Samivel, B., Kesavamurthy, B. and Mehndiratta, M. M. «Application of mobile phones in epilepsy care», International Journal of Epilepsy, 2(01), 028-037, 2015.
Sogamoso, K. V. A. and Parra, O. J. S. «Internet of Things for Epilepsy Detection in Patients», In International Conference on Cooperative Design, Visualization and Engineering, October 2018, pp. 237-244.
Sujatha, K. «Automatic epilepsy detection using hybrid decomposition with multi class support vector method», Multimedia Tools and Applications, 1-20, 2019.
Usman, S. M., Khalid, S. and Aslam, M. H. «Epileptic Seizures Prediction Using Deep Learning Techniques», IEEE Access, Vol 8, pp 39998-40007, 2020.
Ahmed, A., Ahmad, W., Khan, M. J., Siddiqui, S. A. and Cheema, H. M. «A wearable sensor based multi-criteria-decision-system for real-time seizure detection», In 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 2017, pp. 2377-2380.
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P. and Elger, C. E. «Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state», Physical Review E, 64(6), pp 061907, 2001.
Cruz, N. E., Solarte, J. and Gonzalez-Vargas, A. «Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy», In Workshop on Engineering Applications, October 2018, pp. 204-213.
Džaferovi?, E., Vrtagi?, S., Bandi?, L., Kevric, J., Subasi, A. and Qaisar, S. M. «Cloud-based mobile platform for EEG signal analysis», In 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), December 2016, pp. 1-4.
Kolb, R., Pineda, K., Curran, M., Skinner, T., Adegbege, A. and Busha, B. F. «Ambulatory Epileptic Monitoring Device», In 40th Annual Northeast Bioengineering Conference (NEBEC), April 2014, pp. 1-2.
Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L. and Ge, Z. «Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study», In Proceedings of the Australasian Computer Science Week Multiconference, February 2020, pp. 1-8.
Mao, W. L., Fathurrahman, H.I.K.,Lee, Y. and Chang, T. W. «EEG dataset classification using CNN method», In Journal of Physics: Conference Series, Vol. 1456, No. 1, January 2020, pp. 012017.
Marzuki, N. A., Husain, W. and Shahiri, A. M. «MyEpiPal: Mobile Application for Managing, Monitoring and Predicting Epilepsy Patient», In International Conference on Advances in Information and Communication Technology, 2016 December, pp. 383-392.
Menshawy, M. E., Benharref, A. and Serhani, M. «An automatic mobile-health based approach for EEG epileptic seizures detection», Expert systems with applications, 42(20), pp 7157-7174, 2015.
Myers, M. H., Threatt, M., Solies, K. M., McFerrin, B. M., Hopf, L. B., Birdwell, J. D. and Sillay, K. A. «Ambulatory seizure monitoring: From concept to prototype device», Annals of neurosciences, 23(2), pp 100-111, 2016.
Pinho, F., Cerqueira, J., Correia, J., Sousa, N. and Dias, N. «myBrain: a novel EEG embedded system for epilepsy monitoring», Journal of medical engineering & technology, 41(7), pp 564-585, 2017.
Ranganathan, L. N., Chinnadurai, S. A., Samivel, B., Kesavamurthy, B. and Mehndiratta, M. M. «Application of mobile phones in epilepsy care», International Journal of Epilepsy, 2(01), 028-037, 2015.
Sogamoso, K. V. A. and Parra, O. J. S. «Internet of Things for Epilepsy Detection in Patients», In International Conference on Cooperative Design, Visualization and Engineering, October 2018, pp. 237-244.
Sujatha, K. «Automatic epilepsy detection using hybrid decomposition with multi class support vector method», Multimedia Tools and Applications, 1-20, 2019.
Usman, S. M., Khalid, S. and Aslam, M. H. «Epileptic Seizures Prediction Using Deep Learning Techniques», IEEE Access, Vol 8, pp 39998-40007, 2020.
Gupta, S., Ranga, V., & Agrawal, P. (2022). EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(4), 435–452. https://doi.org/10.14201/ADCAIJ2021104435452
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