Deep Learning for Intelligent Identification of Arrhythmias

Arrhythmia Clinical Trial

— ECG-LEARNING  

Official title:

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05967546
• Other study ID #: XJTU1AF2023LSK-170
• Status: Not yet recruiting
• Start date: December 30, 2024
• Est. completion date: December 31, 2028

Study information

• Verified date: April 2024
• Source: First Affiliated Hospital Xi'an Jiaotong University
• Contact: Guoliang Li, M.D.
• Phone: +8613759982523
• Email: liguoliang_med[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 4000
• Est. completion date: December 31, 2028
• Est. primary completion date: August 31, 2028
• Accepts healthy volunteers: No
• Gender: All
• Age group: 3 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Arrhythmia
• Arrhythmias, Cardiac

Intervention

Other:
• Observational
No interventions will be given to patients.

Locations

Country	Name	City	State
China	First Affiliated Hospital of Xi'an Jiantong University	Xi'an	Shaanxi

Sponsors (4)

Lead Sponsor	Collaborator
First Affiliated Hospital Xi'an Jiaotong University	521 Hospital of NORINCO Group, Shaanxi Provincial People's Hospital, Xiangyang Central Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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.