The ECG study using AI
Not Applicable
Recruiting
- Conditions
- Diseases of the circulatory system
- Registration Number
- KCT0007881
- Lead Sponsor
- Inha University Hospital
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Recruiting
- Sex
- All
- Target Recruitment
- 48000
Inclusion Criteria
Normal homostatic ECG of patients diagnosed with atrial fibrillation over 18 years of age with electrocardiogram records that can be extracted from text (XML) files, and normal persons who have not been diagnosed with atrial fibrillation. According to the methods studied in the previous paper, homodynamic ECG in patients with atrial fibrillation is preferably within one and three months before and after using the atrial fibrillation ECG as an indicator.
Exclusion Criteria
This study is a data acquisition study using electrocardiogram, and there is no exclusion standard other than the patient who is inappropriate as a subject in the judgment of the researcher.
Study & Design
- Study Type
- Observational Study
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method In this study, we use deep learning algorithms to predict the patient's clinical picture through electrocardiogram analysis, and ultimately determine the clinical relevance of patients such as cardiac blood rate and heart rate age, and determine the basic baseline characteristics of the patient -age, gender, atrial fibrillation classification, presence or absence of chronic kidney disease, death, hospitalization status, diagnosis date, record date of atrial fibrillation, period from diagnosis to radiofectomy, electrodynamic conversion surgery, dosage type Evaluate, creatine, TG, HDL, LDL, cholesterol, height, weight, blood pressure, high blood pressure, drinking time, exercise, echocardioncardiation values (left ventricular hematocyte count, left atrial size, systolic and diastolic functions, blood flow, etc.)
- Secondary Outcome Measures
Name Time Method