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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Invasive cardiac catheterisation is a common procedure that is carried outbefore surgical intervention. Yet, invasive cardiac diagnostics are full ofrisks, especially for young children. Decades of research has been conducted onthe so called inverse problem of electrocardiography, which can be used toreconstruct Heart Surface Potentials (HSPs) from Body Surface Potentials(BSPs), for non-invasive diagnostics. State of the art solutions to the inverseproblem are unsatisfactory, since the inverse problem is known to be ill-posed.In this paper we propose a novel approach to reconstructing HSPs from BSPsusing a Time-Delay Artificial Neural Network (TDANN). We first design the TDANNarchitecture, and then develop an iterative search space algorithm to find theparameters of the TDANN, which results in the best overall HSP prediction. Weuse real-world recorded BSPs and HSPs from individuals suffering from seriouscardiac conditions to validate our TDANN. The results are encouraging, in thatcoefficients obtained by correlating the predicted HSP with the recordedpatient' HSP approach ideal values.

A machine learning approach to reconstruction of heart surface potentials from body surface potentials