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

Neonatal seizure detection algorithms (SDA) are approaching the benchmark ofhuman expert annotation. Measures of algorithm generalizability andnon-inferiority as well as measures of clinical efficacy are needed to assessthe full scope of neonatal SDA performance. We validated our neonatal SDA on anindependent data set of 28 neonates. Generalizability was tested by comparingthe performance of the original training set (cross-validation) to itsperformance on the validation set. Non-inferiority was tested by assessinginter-observer agreement between combinations of SDA and two human expertannotations. Clinical efficacy was tested by comparing how the SDA and humanexperts quantified seizure burden and identified clinically significant periodsof seizure activity in the EEG. Algorithm performance was consistent betweentraining and validation sets with no significant worsening in AUC (p>0.05, n=28). SDA output was inferior to the annotation of the human expert, however,re-training with an increased diversity of data resulted in non-inferiorperformance ($\Delta\kappa$=0.077, 95% CI: -0.002-0.232, n=18). The SDAassessment of seizure burden had an accuracy ranging from 89-93%, and 87% foridentifying periods of clinical interest. The proposed SDA is approaching humanequivalence and provides a clinically relevant interpretation of the EEG.

Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy