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

Artifacts in the electroencephalogram (EEG) degrade signal quality and impactthe analysis of brain activity. Current methods for detecting artifacts insleep EEG rely on simple threshold-based algorithms that require manualintervention, which is time-consuming and impractical due to the vast volume ofdata that novel mobile recording systems generate. We propose a convolutionalneural network (CNN) model incorporating a convolutional block attention module(CNN-CBAM) to detect and identify the location of artifacts in the sleep EEGwith attention maps. We benchmarked this model against six other machinelearning and signal processing approaches. We trained/tuned all models on 72manually annotated EEG recordings obtained during home-based monitoring from 18healthy participants with a mean (SD) age of 68.05 y ($\pm$5.02). We testedthem on 26 separate recordings from 6 healthy participants with a mean (SD) ageof 68.33 y ($\pm$4.08), with contained artifacts in 4\% of epochs. CNN-CBAMachieved the highest area under the receiver operating characteristic curve(0.88), sensitivity (0.81), and specificity (0.86) when compared to the otherapproaches. The attention maps from CNN-CBAM localized artifacts within theepoch with a sensitivity of 0.71 and specificity of 0.67. This workdemonstrates the feasibility of automating the detection and localization ofartifacts in wearable sleep EEG.

Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms