English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Epilepsy is one of the most common neurological disorders that can bediagnosed through electroencephalogram (EEG), in which the following epilepticevents can be observed: pre-ictal, ictal, post-ictal, and interictal. In thispaper, we present a novel method for epilepsy detection into twodifferentiation contexts: binary and multiclass classification. For featureextraction, a total of 105 measures were extracted from power spectrum,spectrogram, and bispectrogram. For classifier building, eight differentmachine learning algorithms were used. Our method was applied in a widely usedEEG database. As a result, random forest and backpropagation based onmultilayer perceptron algorithms reached the highest accuracy for binary(98.75%) and multiclass (96.25%) classification problems, respectively.Subsequently, the statistical tests did not find a model that would achieve abetter performance than the other classifiers. In the evaluation based onconfusion matrices, it was also not possible to identify a classifier thatstands out in relation to other models for EEG classification. Even so, ourresults are promising and competitive with the findings in the literature.

Binary and Multiclass Classifiers based on Multitaper Spectral Features for Epilepsy Detection