Deep Learning for Intelligent Identification of Arrhythmias

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.

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.

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.