Skip to main content
OpenTrials
Not Yet Recruiting

NCT Number: NCT05967546

Deep Learning for Intelligent Identification of Arrhythmias

This study aims to design and train a deep learning model for the diagnosis of different arrhythmias.

Not Yet Recruiting

Trial opening soon.

Get Notified

Key information

Conditions

Arrhythmia Arrhythmias, Cardiac Cardiovascular Diseases Heart Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

3 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

First Affiliated Hospital of Xi'an Jiantong University

Xi'an, Shaanxi, 710061, China

About this study

This study aims to retrospectively and prospectively collect routine clinical data such as electrocardiograms from patients with arrhythmias who meet the inclusion and exclusion criteria. Then we will design and train a deep learning model to analyse the electrocardiographic features of the arrhythmias, and identify the types of arrhythmias and evaluate the value of the model for the diagnosis of different arrhythmias.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • For retrospective study: 1.Patients with arrhythmia diagnosed by routine surface 12-lead electrocardiogram or Holter; 2.The type of arrhythmia is diagnosed by intracardiac electrophysiological examination.
  • For prospective study: 1.Patients with arrhythmia diagnosed by routine surface 12-lead electrocardiogram or Holter; 2.Intracardiac electrophysiological examination is planned.

Exclusion criteria

  • Lack of routine surface 12-lead electrocardiogram or holter data;
  • Lack of intracardiac electrophysiological examination;
  • Patients refused to sign informed consent and refused to participate in the study.

Treatment and study plan

Observational

Other

No interventions will be given to patients.

Primary outcomes

  1. A deep learning model designed to intelligently identify the types of arrhythmia.

    Time frame: 1 day after the enrollment.

    The model is trained on the training set, the best model and hyperparameters are selected through the verification set, and finally the model results are tested on the test set.

Secondary outcomes

  1. The sensitivity, specificity and accuracy of the deep learning model

    Time frame: 1 day after the enrollment.

    The sensitivity, specificity and accuracy of a deep learning model designed were evaluated by intracardiac electrophysiological examination results to identify patients with arrhythmia from various centers.

Study contacts

Contact information is provided by the study sponsor or research team.

Chaofeng Sun, M.D.

CONTACT

[email protected]

Guoliang Li, M.D.

CONTACT

[email protected]

+8613759982523

Sponsors and collaborators

Lead sponsor

First Affiliated Hospital Xi'an Jiaotong University

Other

Collaborators

  • 521 Hospital of NORINCO Group
  • Shaanxi Provincial People's Hospital
  • Xiangyang Central Hospital

Registry information

Official study title

Deep Learning for Intelligent Identification of Arrhythmias (ECG-LEARNING): an Investigator-initiated, National Multicenter, Retrospective-prospective, Cohort Study

Acronym: ECG-LEARNING

Important dates

Study start
2024
Primary completion
2028
Study completion
2028
First posted
Aug 1, 2023
Registry last updated
Apr 4, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.