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Authors: Gul Khan ; Nadeem Khan ; Wala Saadeh and Muhammad Altaf

Affiliation: Lahore University of Management Sciences (LUMS), Lahore, Pakistan

Keyword(s): Autoencoder, Electroencephalogram (EEG), Feature Selection, Seizure Prediction, Signal Sparsity.

Abstract: Patients with epilepsy are affected with unexpected seizure events, which significantly diminish their quality of life. It is crucial to evaluate whether an epileptic patient’s brain state is indicative of a possible seizure occurrence so that necessary therapy or alarm can be generated on time. If seizures could be predicted before the onset, interventions may be applied to avoid further damage during seizure attack, and patients could take medications or other treatments to prevent seizures from occurring. This research describes a patient-specific technique for predicting epileptic seizures based on a hybrid model. Single layer sparse autoencoder is trained to obtain a aparse representation of the scalp electroencephalogram (EEG) signals. SVM classifier is used to categorize the sparse signal as inter-ictal or pre-ictal. Individual EEG channel analysis for seizure prediction are presented. In addition, various hidden sizes of the autoencoder for optimal sparse representation are a nlyzed.The proposed model evaluates 13 patients from the CHB-MIT dataset and obtains a sensitivity of 98% and an area under the curve (AUC) of 98%. We have evaluated the performance of our hybrid strategy to both deep learning models and conventional procedures. The proposed method outperforms current seizure prediction techniques, proving its efficacy. (More)

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Paper citation in several formats:
Khan, G., Khan, N., Saadeh, W. and Altaf, M. (2023). Using Sparse Representation of EEG Signal from a Shallow Sparse Autoencoder for Epileptic Seizure Prediction. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS; ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 125-132. DOI: 10.5220/0011813400003414

@conference{biosignals23,
author={Gul Khan and Nadeem Khan and Wala Saadeh and Muhammad Altaf},
title={Using Sparse Representation of EEG Signal from a Shallow Sparse Autoencoder for Epileptic Seizure Prediction},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS},
year={2023},
pages={125-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011813400003414},
isbn={978-989-758-631-6},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS
TI - Using Sparse Representation of EEG Signal from a Shallow Sparse Autoencoder for Epileptic Seizure Prediction
SN - 978-989-758-631-6
IS - 2184-4305
AU - Khan, G.
AU - Khan, N.
AU - Saadeh, W.
AU - Altaf, M.
PY - 2023
SP - 125
EP - 132
DO - 10.5220/0011813400003414
PB - SciTePress

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