@inproceedings{c16e90470bf545bc82fd605cb43afb16,
title = "A Two-Layer LSTM Deep Learning Model for Epileptic Seizure Prediction",
abstract = "We propose an efficient seizure prediction model based on a two-layer LSTM using the Swish activation function. The proposed structure performs feature extraction based on the time and frequency domains and uses the minimum distance algorithm as a post-processing step. The proposed model is evaluated on the Melbourne dataset and achieves the highest Area Under Curve (AUC) score of 0.92 and the lowest False Positive Rate (FPR) of 0.147 compared to previous work while having sensitivity and accuracy of 86.8 and 85.1, respectively. The proposed system has a low number of trainable parameters, and thus reducing the complexity of resource-constrained applications.",
keywords = "Deep learning, LSTM, Melbourne dataset, classification, epilepsy seizure prediction, feature extraction, iEEG",
author = "Varnosfaderani, {Shiva Maleki} and Rihat Rahman and Sarhan, {Nabil J.} and Levin Kuhlmann and Eishi Asano and Aimee Luat and Mohammad Alhawari",
note = "Funding Information: This work was supported by Richard Barber Interdisciplinary Research Program. (Corresponding author: Shiva Maleki Varnosfaderani.) Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021 ; Conference date: 06-06-2021 Through 09-06-2021",
year = "2021",
month = jun,
day = "6",
doi = "10.1109/AICAS51828.2021.9458539",
language = "English",
series = "2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021",
}