Speeded up robust features trailed GCN for seizure identification during pregnancy

Authors

  • Geetanjali Nayak GIET University, Gunupur
  • NeelaMadhab Padhy GIET University, Gunupur
  • Tusar Kanti Mishra Manipal Institute of Technology Bengaluru

DOI:

https://doi.org/10.62110/sciencein.jist.2024.v12.810

Keywords:

MRI, pregnancy, Machine learning, seizure identification, EEG signals, brain MRI, Convolutional Neural Network (CNN)

Abstract

In this work, an efficient computational framework has been designed for seizure identification using MRI analysis. The inputs being brain MRI of pregnant women and corresponding outputs being the seizure or no seizure label. The framework is implemented in two phases. First, the informative speeded up robust features (SURF) are extracted from the MRI. Second, these features are further mapped to a graph convolutional neural network (GCN). The maximal clique is generated out of these intermediate features and subjected to convolutional neural network (CNN) architecture for classification. The maximal clique acts as an efficient tool for representing final and fine-tuned feature points through combined graph convolution and thus contributes towards efficient classification. The designed framework is validated through benchmark dataset images presented by NITRC. Experimental evaluation is made on samples of ‘male’, ‘female’ and ‘female with pregnancy’. The overall rate of accuracy stands at 96%, 95%, and 95% respectively.

URN:NBN:sciencein.jist.2024.v12.810

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Author Biographies

  • Geetanjali Nayak, GIET University, Gunupur

    Department of Computer Science and Engineering

  • NeelaMadhab Padhy, GIET University, Gunupur

    Department of Computer Science and Engineering

  • Tusar Kanti Mishra, Manipal Institute of Technology Bengaluru

    Department of Computer Science and Engineering

seizure identification in MRI

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Published

2024-02-12

Issue

Section

Biomedical and Pharmaceutical Sciences

URN

How to Cite

Speeded up robust features trailed GCN for seizure identification during pregnancy. (2024). Journal of Integrated Science and Technology, 12(5), 810. https://doi.org/10.62110/sciencein.jist.2024.v12.810

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