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

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.

Keywords

• epileptic seizure detection
• brain signal preprocessing
• neurowave-net
• eeg
• lstm-cnn
• convolutional neural networks
• long short-term memory