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

In this paper, we propose a novel contrastive learning based deep learningframework for patient similarity search using physiological signals. We use acontrastive learning based approach to learn similar embeddings of patientswith similar physiological signal data. We also introduce a number of neighborselection algorithms to determine the patients with the highest similarity onthe generated embeddings. To validate the effectiveness of our framework formeasuring patient similarity, we select the detection of Atrial Fibrillation(AF) through photoplethysmography (PPG) signals obtained from smartwatchdevices as our case study. We present extensive experimentation of ourframework on a dataset of over 170 individuals and compare the performance ofour framework with other baseline methods on this dataset.

Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal