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/

Global: Zendy Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

—Reliable classification of drowsy stage in EEG signals have attracted attentions from researchers for many years because of large amounts of brain signal noise. Recent studies have demonstrated that the analysis of EEG signals can get benefits from wavelet transform (WT). Despite of this, experiments do not support the effective use of wavelet features for the discrimination of EEG signals because there is much redundant and irrelevant information contained in wavelet coefficients. Furthermore, extraction of useful features from EEG signals for classification is still an open research question. The novel method present in this paper is to extract useful features for classification of EEG signals based on wavelet transform. This method basically consists of two major steps. The first step is extracting energy coefficients from wavelet transform based on Parseval's theorem to represent the distribution of brain signals. The second step focuses on revising weights of energy coefficients to facilitate a classification method. We show that the energy-based features not only capture meaningful information of wavelet transform, but also are useful for classification. We evaluate the proposed method by using the energy-based features to train a neural network for classification of drowsy and alert signals in EEG records. The experimental results conducted on the MIT-BIH Polysomnographic database have shown that the proposed method achieves 90.27% of accuracy compared to wavelet-based methods.

Wavelet Transform Enhancement for Drowsiness Classification in EEG Records Using Energy Coefficient Distribution and Neural Network