Automated Seizure Detection Using Machine Learning Algorithm in Very Large Scale Integration

Authors

  • K. Sri Vijaya Assistant Professor, Department of IT, P V P Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India
  • B. Hari Chandana Associate Professor, Department of CSE, SRIT-AUTONOMOUS-ANANTAPUR, Andhra Pradesh, India
  • D. Sugumar Associate Professor, Department of ECE, Karunya Institute of Technology and Sciences (Deemed to be University), Coimbatore, Tamil Nadu, India
  • Muralidharan J. Associate Professor, Department of ECE, KPR Institute of Engineering and Technology, Arasur, Coimbatore, Tamil Nadu, India
  • I. Mohana Krishna Assistant Professor, K L Business School, KLEF, Vaddeswaram, Andhra Pradesh, India
  • Ramesh Babu P. Associate Professor, Department of Computer Science, College of Engineering and Technology, Wollega University, Nekemte, Oromia Region, Ethiopia.

Keywords:

Seizure detection, Artificial Neural Network, VLSI, Machine Learning

Abstract

The portable automated seizure identification device is so small and portable makes it an especially helpful tool for people who suffer from epilepsy. We propose the use of a VLSI-based automatic seize detection architecture in our proposed system to promote rapid on-chip learning and greater detection rates. The architecture consists of an extractor and an artificial neural network (ANN) module. To produce the time-frequency domain function vector, it first converts the EEG signal into the format of the clinical strip using DWT three-levels, and then it calculates the average absolute value and variance of the four DWT coefficients. Finally, it outputs the function vector in the time-frequency domain. To achieve the highest possible level of productivity from on-chip learning, the classifier is used in conjunction with a Gaussian kernel and a modified version of the sequence minimum optimization method. The results of the study demonstrate that the developed VLSI device reduces the amount of time required to achieve and keep the precision required for detection and recognition.

Downloads

Download data is not yet available.

References

Shanmugam, S., & Dharmar, S. (2022). Very large scale integration implementation of seizure detection system with on‐chip support vector machine classifier. IET Circuits, Devices & Systems, 16(1), 1-12.

Rajashekhar, U., & Harish, H. M. (2022). Automatic diseases detection and classification of EEG signal with pervasive computing using machine learning. International Journal of Pervasive Computing and Communications, (ahead-of-print).

Anita, M., & Kowshalya, A. M. (2024). Automatic epileptic seizure detection using MSA-DCNN and LSTM techniques with EEG signals. Expert Systems with Applications, 238, 121727.

Dhar, P., Garg, V. K., & Rahman, M. A. (2022). Enhanced feature extraction-based CNN approach for epileptic seizure detection from EEG signals. Journal of healthcare engineering, 2022.

Singh, K., & Malhotra, J. (2022). Smart neurocare approach for detection of epileptic seizures using deep learning based temporal analysis of EEG patterns. Multimedia Tools and Applications, 81(20), 29555-29586.

Nafea, M. S., & Ismail, Z. H. (2022). Supervised machine learning and deep learning techniques for epileptic seizure recognition using EEG signals—A systematic literature review. Bioengineering, 9(12), 781.

Massoud, Y. M., Abdelzaher, M., Kuhlmann, L., & Abd El Ghany, M. A. (2023). General and patient-specific seizure classification using deep neural networks. Analog Integrated Circuits and Signal Processing, 1-16.

Du, R., Huang, J., & Zhu, S. (2022, November). EEG-Based Epileptic Seizure Detection Model Using CNN Feature Optimization. In 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1-6). IEEE.

Ahmad, I., Wang, X., Zhu, M., Wang, C., Pi, Y., Khan, J. A., ... & Li, G. (2022). EEG-based epileptic seizure detection via machine/deep learning approaches: A Systematic Review. Computational Intelligence and Neuroscience, 2022.

Sowmya, N., Pradhan, S., Biswal, P. K., Panda, S. K., & Misra, V. P. (2022). Epileptic seizure detection using deep learning architecture. In Smart and Sustainable Technologies: Rural and Tribal Development Using IoT and Cloud Computing: Proceedings of ICSST 2021 (pp. 239-248). Singapore: Springer Nature Singapore.

Hamza, M. A., Negm, N., Al-Otaibi, S., Alhussan, A. A., Al Duhayyim, M., Al-Yarimi, F. A. M., ... & Yaseen, I. (2022). Evolutionary Algorithm with Machine Learning Based Epileptic Seizure Detection Model. Computers, Materials & Continua, 72(3).

Hamza, M. A., Negm, N., Al-Otaibi, S., Alhussan, A. A., Al Duhayyim, M., Al-Yarimi, F. A. M., ... & Yaseen, I. (2022). Evolutionary Algorithm with Machine Learning Based Epileptic Seizure Detection Model. Computers, Materials & Continua, 72(3).

Reddy, B.R.S., Saxena, A.K., Pandey, B.K., Gupta, S., Gurpur, S., Dari, S.S., Dhabliya, D. Machine learning application for evidence image enhancement (2023) Handbook of Research on Thrust Technologies? Effect on Image Processing, pp. 25-38.

Downloads

Published

05.12.2023

How to Cite

Vijaya, K. S. ., Chandana, B. H. ., Sugumar, D. ., J., M. . ., Krishna, I. M. ., & P., R. B. . (2023). Automated Seizure Detection Using Machine Learning Algorithm in Very Large Scale Integration. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 01–06. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4002

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

License

Creative Commons 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.