Vol. 23 No. 2 (2024): Mapana Journal of Sciences
Research Articles

Adaptive Bi-LSTM-based Epileptic Seizure Prediction from EEG Signals Using Deep Learning Algorithm

PDF
  • Jamunadevi C,
  • Arul P
Jamunadevi C
Government Arts College (Affiliated to Bharathidasan University, Trichy-24)Tiruchirappalli-620 022
DOI: https://doi.org/10.12723/mjs.69.4

Published 2024-07-04

Keywords

  • Epileptic seizure,
  • Bi-LSTM,
  • Electroencephalogram (EEG),
  • convolutional neural network (CNN)

Copyright (c) 2024

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

Millions of people worldwide who suffer from epilepsy are severely impacted by frequent, erratic seizures. Preventing seizures from occurring could greatly enhance patient care and safety. This paper presents an approach to real-time epileptic seizure prediction utilizing a unique kind of neural network known as Bidirectional Long Short-Term Memory (Bi-LSTM). The existing multidimensional CNN deep neural network models have average accuracy for multi-channel EEG data in predicting seizures. The proposed model was selected for its excellent ability to interpret complex seizure patterns from both historical and prospective data. We took advantage of a big dataset of real-time EEG recordings of brain activity. The testing yielded the results with the approach of 98% accuracy in predicting seizures. Using the Bi-LSTM model in real-time systems has shown that it can make precise predictions quickly, offering hope for improving the lives of those with epilepsy.

PDF

References

  1. Xiaoyan Wei ,Xiaojun Cao,zhen zhang, Yi Zhou, “Epileptic seizure prediction from multivariate sequential signals using multidimensional convolution network”, 2022.
  2. Ranjan Jana , Imon Mukherjee, “Deep learning based efficient epileptic seizure prediction with EEG channel optimization “,2021.
  3. Ahmed M. Abdelhameed and Magdy Bayoumi, “An Efficient Deep Learning System for Epileptic Seizure Prediction”,2021.
  4. Manhua Jia1, Wenjian Liu2, Junwei Duan3, Long Chen4,C. L. Philip Chen5, Qun Wang1* and Zhiguo Zhou1*, “Efficient graph convolutional networks for seizure prediction using scalp EEG”,2022.
  5. Abhijeet Bhattacharya, “Automatic Seizure Prediction using CNN and LSTM”, 2021.
  6. Ziwei Tian, Bingliang Hu ,Yang Si and Quan Wang, “Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifie.”, 2023.
  7. Mrutyunjaya Sahani, Susanta Kumar Rout and Pradipta Kishor Dash,” Epileptic Seizure Predection Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals”,2021.
  8. Banu Priya Prathaban , Ramachandran Balasubramanian,” Dynamic learning framework for epileptic seizure prediction using sparsity based EEG Reconstruction with Optimized CNN classifier”,2021.
  9. S.Raghu, N.Sriram, Y.Temel, S.V.Rao, A.S.Hegde & P. L. Kubben, “Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier”,2019.
  10. Asmaa Hamad, Essam H. Houssein, Aboul Ella Hassanien & Aly A. Fahmy, “Hybrid Grasshopper Optimization Algorithm and Support Vector Machines for Automatic Seizure Detection in EEG Signals”,2018.
  11. Zheng Zhang, Xin Li , Fengji Geng , and Kejie Huang, “A Semi-Supervised Few-Shot Learning Model for Epileptic Seizure Detection”,2021.
  12. “Yonghua Yang, Rani A. Sarkis, Rima El Atrache , Tobias Loddenkemper and Christian Meisel, “Video-Based Detection of Generalized Tonic-Clonic Seizures Using Deep Learning”, 2021.
  13. Kosuke Fukumori , Noboru Yoshida, Hidenori Sugano, Madoka Nakajima, & Toshihisa Tanaka, “Epileptic Spike Detection Using Neural Networks With Linear-Phase Convolutions”,2022.
  14. Gaowei Xu, Tianhe Ren, Yu Chen & Wenliang Che, “A One-Dimensional CNN-LSTM Model for Epileptic Seizure Recognition Using EEG Signal Analysis”,2020.
  15. Cao Xiao, Shouyi Wang, Leon Iasemidis, Stephen Wong, Wanpracha Art Chaovalitwongse, “An Adaptive Pattern Learning Framework to Personalize Online Seizure Prediction”,2021.
  16. Syed Muhammad Usman 1, Shehzad Khalid1, And Muhammad Haseeb Aslam2,
  17. “Epileptic Seizures Prediction Using Deep Learning Techniques”,2020.
  18. Theekshana Dissanayake, Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes,” Patient-independent Epileptic Seizure Prediction using Deep Learning Models”,2020.
  19. Yankun Xu, Jie Yang, Shiqi Zhao, Hemmings Wu, Mohamad Sawan,” An End-to-End Deep Learning Approach for Epileptic Seizure Prediction”,2020.
  20. Hisham Daoud, Phillip Williams, Magdy Bayoumi,” IoT based Efficient Epileptic Seizure Prediction System Using Deep Learning”,2020.
  21. Shiqi Zhao, Jie Yang, Yankun Xu, Mohamad Sawan,” Binary Single-Dimensional Convolutional Neural Network for Seizure Prediction”,2020.