Detection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordings

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dc.contributor.authorLagos Barzola, Manuel Avelino
dc.date.accessioned2025-03-08T02:07:31Z
dc.date.available2025-03-08T02:07:31Z
dc.date.issued2023
dc.description.abstractAccording to the World Health Organization, epilepsy affects more than 50 million people in the world, and specifically, 80% of them live in developing countries. Therefore, epilepsy has become among the major public issue for many governments and deserves to be engaged. Epilepsy is characterized by uncontrollable seizures in the subject due to a sudden abnormal functionality of the brain. Recurrence of epilepsy attacks change people’s lives and interferes with their daily activities. Although epilepsy has no cure, it could be mitigated with an appropriated diagnosis and medication. Usually, epilepsy diagnosis is based on the analysis of an electroencephalogram (EEG) of the patient. However, the process of searching for seizure patterns in a multichannel EEG recording is a visual demanding and time consuming task, even for experienced neurologists. Despite the recent progress in automatic recognition of epilepsy, the multichannel nature of EEG recordings still challenges current methods. In this work, a new method to detect epilepsy in multichannel EEG recordings is proposed. First, the method uses convolutions to perform channel fusion, and next, a self-attention network extracts temporal features to classify between interictal and ictal epilepsy states. The method was validated in the public CHB-MIT dataset using the k-fold cross-validation and achieved 99.74% of specificity and 99.15% of sensitivity, surpassing current approaches.es_PE
dc.description.uriArtículo Científicoes_PE
dc.formatapplication/pdf
dc.identifier.otherArtículo Científico 9_Lag
dc.identifier.urihttps://repositorio.unsch.edu.pe/handle/20.500.14612/7547
dc.language.isoenes_PE
dc.publisherUniversidad Nacional de San Cristóbal de Huamangaes_PE
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceUniversidad Nacional de San Cristóbal de Huamangaes_PE
dc.sourceRepositorio Institucional - UNSCHes_PE
dc.subjectEpilepsyes_PE
dc.subjectEpilepsy detectiones_PE
dc.subjectEEGes_PE
dc.subjectEEG channel fusiones_PE
dc.subjectConvolutional neural networkes_PE
dc.subjectSelf-attentiones_PE
dc.titleDetection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordingses_PE
dc.typeinfo:eu-repo/semantics/articleen_US
renati.advisor.orcidhttps://orcid.org/0000-0001-8078-755X
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