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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT05967546
Recruitment Status : Not yet recruiting
First Posted : August 1, 2023
Last Update Posted : April 4, 2024
Sponsor:
Collaborators:
521 Hospital of NORINCO Group
Shaanxi Provincial People's Hospital
Xiangyang Central Hospital
Information provided by (Responsible Party):
First Affiliated Hospital Xi'an Jiaotong University

Study Description
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Brief Summary:
This study aims to design and train a deep learning model for the diagnosis of different arrhythmias.

Condition or disease Intervention/treatment
Arrhythmia 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 Design
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Study Type : Observational
Estimated Enrollment : 4000 participants
Observational Model: Cohort
Time Perspective: Other
Official Title: Deep Learning for Intelligent Identification of Arrhythmias (ECG-LEARNING): an Investigator-initiated, National Multicenter, Retrospective-prospective, Cohort Study
Estimated Study Start Date : December 30, 2024
Estimated Primary Completion Date : August 31, 2028
Estimated Study Completion Date : December 31, 2028

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Arrhythmia

Groups and Cohorts
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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.


Outcome Measures
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Primary Outcome Measures :
  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 Outcome Measures :
  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.


Eligibility Criteria
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Ages Eligible for Study:   3 Years and older   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Patients diagnosed with arrhythmia by twelve-lead electrocardiogram or Holter.
Criteria

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.
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05967546


Contacts
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Contact: Guoliang Li, M.D. +8613759982523 liguoliang_med@163.com
Contact: Chaofeng Sun, M.D. cfsun1@mail.xjtu.edu.cn

Locations
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China, Shaanxi
First Affiliated Hospital of Xi'an Jiantong University
Xi'an, Shaanxi, China, 710061
Sponsors and Collaborators
First Affiliated Hospital Xi'an Jiaotong University
521 Hospital of NORINCO Group
Shaanxi Provincial People's Hospital
Xiangyang Central Hospital
Investigators
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Principal Investigator: Guoliang Li, M.D. First Affiliated Hospital Xi'an Jiaotong University
More Information
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Responsible Party: First Affiliated Hospital Xi'an Jiaotong University
ClinicalTrials.gov Identifier: NCT05967546    
Other Study ID Numbers: XJTU1AF2023LSK-170
First Posted: August 1, 2023    Key Record Dates
Last Update Posted: April 4, 2024
Last Verified: April 2024

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by First Affiliated Hospital Xi'an Jiaotong University:
Arrhythmia
Additional relevant MeSH terms:
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Arrhythmias, Cardiac
Heart Diseases
Cardiovascular Diseases
Pathologic Processes