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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Electroencephalography (EEG) is a method to record the electrical signals inthe brain. Recognizing the EEG patterns in the sleeping brain gives insightsinto the understanding of sleeping disorders. The dataset under considerationcontains EEG data points associated with various physiological conditions. Thisstudy attempts to generalize the detection of particular patterns associatedwith the Non-Rapid Eye Movement (NREM) sleep cycle of the brain using a machinelearning model. The proposed model uses additional feature engineering toincorporate sequential information for training a classifier to predict theoccurrence of Cyclic Alternating Pattern (CAP) sequences in the sleep cycle,which are often associated with sleep disorders.

A machine learning model for identifying cyclic alternating patterns in the sleeping brain