An electrocardiogram study using artificial intelligence in the identification of arrhythmia resection site

Not Applicable

Conditions: Diseases of the circulatory system

Registration Number: KCT0009317

Lead Sponsor: Inha University Hospital

Brief Summary: Not available

Detailed Description: Not available

Recruitment & Eligibility

Status: ot yet recruiting

Sex: All

Target Recruitment: 1500

Inclusion Criteria

Arrhythmia patients with electrocardiogram records, electrophysiological tests, and arrhythmia resection records in INHA university hospital.
1)paroxysmal supraventricular tachycardia(PSVT): Normal sinus rhythm and tachycardia in patients with atrioventricular nodular regression tachycardia and atrioventricular regression tachycardia by electrophysiological examination and arrhythmia resection

2)Wolf-Parkinson-White Syndrome (WPW): WPW electrocardiogram in patients with proven parenteral insufficiency by electrophysiological examination and arrhythmia resection

3)Ventricular premature contraction (PVC): Electrical physiology and arrhythmia resection with ventricular premature contraction (PVC) including early contraction in patients with proven location of ventricular premature contraction

4)Ventricular tachycardia (VT): An electrophysiological examination and arrhythmia resection were performed with ventricular tachycardia (VT) to prove the location of ventricular tachycardia

Exclusion Criteria

The above study is a data collection study using electrocardiogram, and there are no exclusion criteria other than patients who are unsuitable as subjects according to the researcher's judgment

Study & Design

Study Type: Observational Study

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Development of an electrocardiogram prediction program using a 12-lead electrocardiogram artificial intelligence algorithm by encrypting and extracting raw data from cardiovascular disease patients

Secondary Outcome Measures

Name	Time	Method
(Developed AI-based electrocardiogram prediction program) Interpreting 12-lead ECG using AI provides important clues to the preparation and execution of resection procedures for WPW syndrome, PVC and VT, helping EP professionals in safety and efficiency