Key result
A binary neural classifier using an ANN MLP separated epileptiform events from eye blink artifacts in EEG data with 85.9% sensitivity and 87.1% specificity.
Why the study?
Does a binary neural classifier accurately separate epileptiform events from eye blink artifacts in raw EEG data?
Does a binary neural classifier accurately separate epileptiform events from eye blink artifacts in raw EEG data?
Answers are grounded in this paper and cite their sources.
A binary neural classifier using an ANN MLP can effectively separate epileptiform events from eye blink artifacts in EEG signals with high sensitivity and specificity.
This work presents the study, the development and the evaluation of a binary neural classifier to separate the epileptiform events (spike and sharp wave) and eye blink artifacts in electroencephalography exams (EEG). The eye blink is the main artifact that affects the performance of the automatic systems for identification of epileptiform events in EEG signals. The methodology for the development of the binary neural classifier through an ANN MLP is approached. The performance evaluation of the classifier is realized through the statistic index, performance index and ROC curve with performance criterion. With the EER criterion was obtained sensitivity of 85.9%, specificity of 87.1%, positive selectivity of 86.7% and negative selectivity of 86.3%.
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Sovierzoski et al. (2009) studied Epileptiform events and eye blink artifacts in EEG. Binary neural classifier (ANN MLP) was evaluated on Separation of epileptiform events and eye blink artifacts. A binary neural classifier using an ANN MLP separated epileptiform events from eye blink artifacts in EEG data with 85.9% sensitivity and 87.1% specificity.
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