AMIA Annu Symp Proc - A discriminative approach to EEG seizure detection.

Tópicos

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Resumo

Seizures are abnormal sudden discharges in the brain with signatures represented in electroencephalograms (EEG). The efficacy of the application of speech processing techniques to discriminate between seizure and non-seizure states in EEGs is reported. The approach accounts for the challenges of unbalanced datasets (seizure and non-seizure), while also showing a system capable of real-time seizure detection. The Minimum Classification Error (MCE) algorithm, which is a discriminative learning algorithm with wide-use in speech processing, is applied and compared with conventional classification techniques that have already been applied to the discrimination between seizure and non-seizure states in the literature. The system is evaluated on 22 pediatric patients multi-channel EEG recordings. Experimental results show that the application of speech processing techniques and MCE compare favorably with conventional classification techniques in terms of classification performance, while requiring less computational overhead. The results strongly suggests the possibility of deploying the designed system at the bedside.

Resumo Limpo

seizur abnorm sudden discharg brain signatur repres electroencephalogram eeg efficaci applic speech process techniqu discrimin seizur nonseizur state eeg report approach account challeng unbalanc dataset seizur nonseizur also show system capabl realtim seizur detect minimum classif error mce algorithm discrimin learn algorithm wideus speech process appli compar convent classif techniqu alreadi appli discrimin seizur nonseizur state literatur system evalu pediatr patient multichannel eeg record experiment result show applic speech process techniqu mce compar favor convent classif techniqu term classif perform requir less comput overhead result strong suggest possibl deploy design system bedsid

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