NeuroWave-Net: Enhancing epileptic seizure detection from EEG brain signals via advanced convolutional and long short-term memory networks

Md. Mehedi Hassan; Rezuana Haque; Sheikh Mohammed Shariful Islam; Hossam Meshref; Roobaea Alroobaea; Mehedi Masud; Anupam Kumar Bairagi; Md. Mehedi Hassan; Rezuana Haque; Sheikh Mohammed Shariful Islam; Hossam Meshref; Roobaea Alroobaea; Mehedi Masud; Anupam Kumar Bairagi

doi:10.3934/bioeng.2024006

AIMS Bioengineering

2024, Volume 11, Issue 1: 85-109. doi: 10.3934/bioeng.2024006

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Research article

NeuroWave-Net: Enhancing epileptic seizure detection from EEG brain signals via advanced convolutional and long short-term memory networks

1.
Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
2.
Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong, Bangladesh
3.
Institute for Physical Activity and Nutrition, Deakin University, Melbourne, VIC 3125, Australia
4.
Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

Received: 03 January 2024 Revised: 25 March 2024 Accepted: 25 March 2024 Published: 15 April 2024

This study presented a new approach to seizure classification utilizing electroencephalogram (EEG) data. We introduced the NeuroWave-Net, an innovative hybrid model that seamlessly integrates convolutional neural networks (CNN) and long short-term memory (LSTM) architectures. Unlike conventional methods, our model capitalized on CNN's proficiency in feature extraction and LSTM's prowess in classifying seizure. The key strength of the NeuroWave-Net lies in its ability to combine these distinct architectures, synergizing their capabilities for enhanced accuracy in identifying seizure conditions within EEG data. Our proposed model exhibited outstanding performance, achieving a classification accuracy of 99.48%. This study contributed to the advancement of seizure classification models, providing a robust and streamlined approach for accurate categorization within EEG datasets. NeuroWave-Net stands as a testament to the potential of hybrid neural network architectures in neurological diagnostics.
- epileptic seizure detection,
- brain signal preprocessing,
- NeuroWave-Net,
- EEG,
- LSTM-CNN,
- convolutional neural networks,
- long short-term memory
Citation: Md. Mehedi Hassan, Rezuana Haque, Sheikh Mohammed Shariful Islam, Hossam Meshref, Roobaea Alroobaea, Mehedi Masud, Anupam Kumar Bairagi. NeuroWave-Net: Enhancing epileptic seizure detection from EEG brain signals via advanced convolutional and long short-term memory networks[J]. AIMS Bioengineering, 2024, 11(1): 85-109. doi: 10.3934/bioeng.2024006

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Abstract

This study presented a new approach to seizure classification utilizing electroencephalogram (EEG) data. We introduced the NeuroWave-Net, an innovative hybrid model that seamlessly integrates convolutional neural networks (CNN) and long short-term memory (LSTM) architectures. Unlike conventional methods, our model capitalized on CNN's proficiency in feature extraction and LSTM's prowess in classifying seizure. The key strength of the NeuroWave-Net lies in its ability to combine these distinct architectures, synergizing their capabilities for enhanced accuracy in identifying seizure conditions within EEG data. Our proposed model exhibited outstanding performance, achieving a classification accuracy of 99.48%. This study contributed to the advancement of seizure classification models, providing a robust and streamlined approach for accurate categorization within EEG datasets. NeuroWave-Net stands as a testament to the potential of hybrid neural network architectures in neurological diagnostics.

Abbreviations

EEG

Electroencephalogram

CNN

Convolutional neural network

LSTM

Long short-term memory

Random forest

SVM

Support vector machine

PSO

Particle swarm optimization

1D CNN

1-Dimensional convolutional neural networks

2D CNN

2-Dimensional convolutional neural networks

DCNN

Deep neural network

iEEG

Intracranial electroencephalography

C-LSTM

Contextual long short-term memory

BiLSTM

Bidirectional long short-term memory

ReLU

Rectified linear unit

Conv1D

1D convolution layer

Acknowledgments

The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-04). Also we extend our sincere gratitude to the ICT Division, Ministry of Post, Telecommunication, and Information Technology, Government of Bangladesh for their support, enabling the research conducted under the ICT fellowship program.

Conflict of interest

The authors have no conflict of interest.

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