Artif Intell Med - Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing.

Tópicos

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Resumo

TRODUCTION: In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training.MATERIALS: The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours from 15 kainate-induced temporal-lobe epilepsy rats.METHODS: During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques.RESULTS: A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%.CONCLUSION: Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG.

Resumo Limpo

troduct paper propos techniqu base reservoir comput rc mark epilept seizur intracrani electroencephalogram eeg rat rc recurr neural network train techniqu shown possess good general properti limit trainingmateri system evalu data contain two differ seizur type absenc seizur genet absenc epilepsi rat strasbourg gaer tonicclon seizur kainateinduc temporallob epilepsi rat dataset consist hour gaer hour kainateinduc temporallob epilepsi ratsmethod preprocess stage sever featur extract eeg featur select algorithm select best featur present input rcbase classif algorithm classifi output algorithm twothreshold techniqu use techniqu compar stateoftheart techniquesresult balanc error rate ber achiev data gaer kainat rat respect result sensit specif respect stateoftheart techniqu gaer achiev ber wherea best techniqu detect tonicclon seizur achiev ber conclus method outperform uptod techniqu paramet need optim limit train set therefor suit automat aid epilepsi research abl elimin tedious manual review annot eeg

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