Deep Learning for Intelligent Identification of Arrhythmias (ECG-LEARNING)

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

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

Study Overview

• Status: Not yet recruiting
• Conditions: Arrhythmia
• Intervention / Treatment: Other: Observational

Detailed Description

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.

• Study Type: Observational
• Enrollment (Estimated): 4000

Contacts and Locations

• Study Contact: Name: Guoliang Li, M.D.; Phone Number: +8613759982523; Email: liguoliang_med@163.com
• Study Contact Backup: Name: Chaofeng Sun, M.D.; Email: cfsun1@mail.xjtu.edu.cn

Study Locations

• China
  • Shaanxi
    • Xi'an, Shaanxi, China, 710061
      • First Affiliated Hospital of Xi'an Jiantong University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients diagnosed with arrhythmia by twelve-lead electrocardiogram or Holter.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Experimental Group; ECG data and clinical data from this group of arrhythmia patients will be used to build a deep learning model.	Other: Observational; No interventions will be given to patients.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
A deep learning model designed to intelligently identify the types of arrhythmia.	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.	1 day after the enrollment.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The sensitivity, specificity and accuracy of the deep learning model	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.	1 day after the enrollment.

Collaborators and Investigators

• Sponsor: First Affiliated Hospital Xi'an Jiaotong University
• Collaborators: Shaanxi Provincial People's Hospital; 521 Hospital of NORINCO Group; Xiangyang Central Hospital
• Investigators: Principal Investigator: Guoliang Li, M.D., First Affiliated Hospital Xi'an Jiaotong University

Study record dates

Study Major Dates

• Study Start (Estimated): December 30, 2024
• Primary Completion (Estimated): August 31, 2028
• Study Completion (Estimated): December 31, 2028

Study Registration Dates

• First Submitted: July 6, 2023
• First Submitted That Met QC Criteria: July 21, 2023
• First Posted (Actual): August 1, 2023

Study Record Updates

• Last Update Posted (Actual): April 4, 2024
• Last Update Submitted That Met QC Criteria: April 2, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Keywords: Arrhythmia
• Additional Relevant MeSH Terms: Pathologic Processes; Heart Diseases; Cardiovascular Diseases; Arrhythmias, Cardiac
• Other Study ID Numbers: XJTU1AF2023LSK-170

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No