Please use this identifier to cite or link to this item: https://observatorio.fm.usp.br/handle/OPI/32931
Title: Epileptic Seizure Prediction from EEG Signals Using Unsupervised Learning and a Polling-Based Decision Process
Authors: KITANO, Lucas Aparecido SilvaSOUSA, Miguel Angelo AbreuSANTOS, Sara DerestePIRES, RicardoTHOME-SOUZA, SigrideCAMPO, Alexandre Brincalepe
Citation: ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT II, v.11140, p.117-126, 2018
Abstract: Epilepsy is a central nervous system disorder defined by spontaneous seizures and may present a risk to the physical integrity of patients due to the unpredictability of the seizures. It affects millions of people worldwide and about 30% of them do not respond to anti-epileptic drugs (AEDs) treatment. Therefore, a better seizure control with seizures prediction methods can improve their quality of life. This paper presents a patient-specific method for seizure prediction using a preprocessing wavelet transform associated to the Self-Organizing Maps (SOM) unsupervised learning algorithm and a polling-based method. Only 20 min of 23 channels scalp electroencephalogram (EEG) has been selected for the training phase for each of nine patients for EEG signals from the CHB-MIT public database. The proposed method has achieved up to 98% of sensitivity, 88% of specificity and 91% of accuracy. For each subsequence of EEG data received, the system takes less than one second to estimate the patient state, regarding the possibility of an impending seizure.
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Comunicações em Eventos - HC/IPq
Instituto de Psiquiatria - HC/IPq


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