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

We present a novel computerized fetal heart rate intrapartum algorithm forearly and individualized prediction of fetal cardiovascular decompensation, akey event in the causal chain leading to brain injury. This real-time machinelearning algorithm performs well on noisy fetal heart rate data and requires ~2hours to train on the individual fetal heart rate tracings in the first stageof labor; once trained, the algorithm predicts the event of fetalcardiovascular decompensation with 92% sensitivity. We show that thealgorithm's performance suffers reducing sensitivity to 67% when the fetalheart rate is acquired at the sampling rate of 4 Hz used in ultrasoundcardiotocographic monitors compared to the electrocardiogram(ECG)-derivedsignals as can be acquired from maternal abdominal ECG.

frontiers in pediatrics

Fetal cardiovascular decompensation during labor predicted from the  individual heart rate: a prospective study in fetal sheep near term and the  impact of low sampling rate