Deep Learning for Intelligent Identification of Arrhythmias

NCT05967546 | Not Yet Recruiting

• 1 other identifier: XJTU1AF2023LSK-170
• Study Type: observational
• Target: 4,000
• Locations: 1 country
• Sites: 1

Brief Summary

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

Conditions

Arrhythmia

Keywords

Arrhythmia

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• The sensitivity, specificity and accuracy of the deep learning model

  1 day after the enrollment.

Study Arms (1)

Experimental Group

ECG data and clinical data from this group of arrhythmia patients will be used to build a deep learning model.

Other: Observational

Interventions

Observational OTHER

No interventions will be given to patients.

Experimental Group

Eligibility Criteria

• Age: 3 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• First Affiliated Hospital Xi'an Jiaotong University: lead
• 521 Hospital of NORINCO Group: collaborator
• Shaanxi Provincial People's Hospital: collaborator
• Xiangyang Central Hospital: collaborator

Study Sites (1)

First Affiliated Hospital of Xi'an Jiantong University

Xi'an, Shaanxi, 710061, China

Location

MeSH Terms

Conditions

Arrhythmias, Cardiac

Interventions

Watchful Waiting

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Outcome Assessment, Health Care; Outcome and Process Assessment, Health Care; Quality of Health Care; Health Services Administration

Study Officials

• Guoliang Li, M.D.

  First Affiliated Hospital Xi'an Jiaotong University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Guoliang Li, M.D.

CONTACT

+8613759982523; liguoliang_med@163.com

Chaofeng Sun, M.D.

CONTACT

cfsun1@mail.xjtu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 6, 2023
• First Posted: August 1, 2023
• Study Start: December 30, 2024
• Primary Completion (Estimated): August 31, 2028
• Study Completion (Estimated): December 31, 2028
• Last Updated: April 4, 2024

Record last verified: 2024-04

Locations

CN (1)