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\begin{document}

\title{Enhancing EEG Signals Classification using LSTM-CNN Architecture}



\author[1]{Swaleh Omari}%
\author[1]{Michael Kimwele}%
\author[2]{Akeem Olowolayemo}%
\author[3]{Dennis Kaburu}%
\affil[1]{Jomo Kenyatta University of Agriculture and Technology}%
\affil[2]{International Islamic University Malaysia}%
\affil[3]{Jomo Kenyatta University of Agriculture and Technology College of Engineering and Technology}%


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  \date{May 23, 2024}


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\begin{abstract}
Epilepsy is a condition that disrupts normal brain function and
sometimes leads to seizures, unusual sensations, and temporary loss of
awareness. Electroencephalograph (EEG) records are commonly used for
diagnosing epilepsy, but traditional analysis is subjective and prone to
misclassification. Previous studies applied Deep Learning (DL)
techniques to improve EEG classification, but their performance has been
limited due to dynamic and non-stationary nature of EEG structure. In
this paper, we propose a multi-channel EEG classification model called
LConvNet, which combines Convolutional Neural Networks (CNN) for spatial
feature extraction and Long Short-Term Memory (LSTM) for capturing
temporal dependencies. The model is trained using open source secondary
EEG data from Temple University Hospital (TUH) to distinguish between
epileptic and healthy EEG signals. Our model achieved an impressive
accuracy of 97\%, surpassing existing EEG classification models used in
similar tasks such as EEGNet, DeepConvNet and ShallowConvNet that had
86\%, 96\% and 78\% respectively. Furthermore, our model demonstrated
impressive performance in terms of trainability, scalability and
parameter efficiency during additional evaluations.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Enhancing EEG Classification with LSTM-CNN Architecture_Swaleh_Manuscript_20230619.docx`  available at \url{https://authorea.com/users/631387/articles/650757-enhancing-eeg-signals-classification-using-lstm-cnn-architecture}

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