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.

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References

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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://ijisae.org/index.php/IJISAE/article/view/4002

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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