Arrhythmia
Conditions
Keywords
Arrhythmia
Brief summary
This study aims to design and train a deep learning model for the diagnosis of different arrhythmias.
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.
Interventions
OTHERObservational
No interventions will be given to patients.
Sponsors
521 Hospital of NORINCO Group
Shaanxi Provincial People's Hospital
Xiangyang Central Hospital
First Affiliated Hospital Xi'an Jiaotong University
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
3 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| A deep learning model designed to intelligently identify the types of arrhythmia. | 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
| Measure | Time frame | Description |
|---|---|---|
| The sensitivity, specificity and accuracy of the deep learning model | 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. |
Countries
China
Contacts
Primary ContactGuoliang Li, M.D.
Backup ContactChaofeng Sun, M.D.
Outcome results
None listed