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

This work reports a multilead  QT  interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including  QRS  detection as well as an accurate multilead  QT  interval detection algorithm using support vector machines (SVMs). Two fiducial points ( Q  ini  and  T  end ) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the  P ,  QRS , and  T  waves. The  QT  interval is estimated by updating the  QT  interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the  QT  interval measurement algorithm is attained using the Physionet PTB diagnostic ECG database showing a percent error of 2.60 ± 2.25 msec with respect to the database annotations. The usefulness of this software tool is also tested by considering the analysis of the ECG signals for a group of 60 patients acquired using our digital electrocardiograph. In this case, the validation is performed by comparing the estimated  QT  interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of 2.49 ± 1.99 msec.

ECG Multilead QT Interval Estimation Using Support Vector Machines