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

Monitoring of prevalent airborne diseases such as COVID-19 characteristicallyinvolves respiratory assessments. While auscultation is a mainstream method forpreliminary screening of disease symptoms, its utility is hampered by the needfor dedicated hospital visits. Remote monitoring based on recordings ofrespiratory sounds on portable devices is a promising alternative, which canassist in early assessment of COVID-19 that primarily affects the lowerrespiratory tract. In this study, we introduce a novel deep learning approachto distinguish patients with COVID-19 from healthy controls given audiorecordings of cough or breathing sounds. The proposed approach leverages anovel hierarchical spectrogram transformer (HST) on spectrogram representationsof respiratory sounds. HST embodies self-attention mechanisms over localwindows in spectrograms, and window size is progressively grown over modelstages to capture local to global context. HST is compared againststate-of-the-art conventional and deep-learning baselines. Demonstrations oncrowd-sourced multi-national datasets indicate that HST outperforms competingmethods, achieving over 83% area under the receiver operating characteristiccurve (AUC) in detecting COVID-19 cases.

COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers