Atrial Flutter, Left Bundle-Branch Block, Left Ventricular Hypertrophy, Long QT Syndrome, Myocardial Infarction, Premature Atrial Complexes, Premature Ventricular Complexes, Right Bundle-Branch Block, Sinus Bradycardia, Sinus Tachycardia
Conditions
Brief summary
Chang Gung ECG Abnormality Detection Software is a is an artificial intelligence medical signal analysis software that detect whether patients have abnormal ECG signals of 14 diseases by static 12-lead ECG. The 14 diseases were * Long QT syndrome * Sinus bradycardia * Sinus Tachycardia * Premature atrial complexes * Premature ventricular complexes * Atrial Flutter, Right bundle branch block * Left bundle branch block * Left Ventricular hypertrophy * Anterior wall Myocardial Infarction * Septal wall Myocardial Infarction * Lateral wall Myocardial Infarction * Inferior wall Myocardial Infarction * Posterior wall Myocardial Infarction The main purpose of this study is to verify whether Chang Gung ECG Abnormality Detection Software can correctly identify abnormal ECG signals among patients of 14 diseases. The interpretation standard is the consensus of 3 cardiologists. The results of the software analysis will be used to evaluate the performance of the primary and secondary evaluation indicators.
Detailed description
Detailed procedure: 1. Sample source: This is a retrospective study, and the data comes from the Chang Gung Medical Research Database(CGRD) which was an database form 6 hospitals of Chang Gung Memorial hospital. We collected de-identified static 12-lead ECG data from the database during 2006.01.01\ 2019.12.31, and the length of the ECG was 10 seconds. 2. Sampling: In this experiment, the training dataset and the test dataset ECG were separated. Afterwards, the ECG signals are stratified according to the distribution as the test sample, and all abnormal ECG signals of 14 diseases will be independently sampled from the ECG database of the test set. 3. Confirmation criteria: The ECG data will be preliminarily screened and selected by the inclusion and exclusion criteria and compiled serial numbers. Then, a cardiologist confirms that the sampling results of the ECG data do not include the exclusion criteria again. 4. Physician interpretation: The ECG data will be converted into graphic files and submitted to 3 cardiologists for interpretation abnormal ECG signals of 14 related diseases. The results will be used as the standard of this study (Reference). 5. Software interpretation: After confirming the test standard, input the ECG signal into Chang Gung ECG Abnormality Detection Software to analyze abnormal ECG signals of 14 diseases and interpret each ECG data. 6. Statistical analysis: After the software interpretation is completed, it will be compared with the results of the physician's interpretation and analyze the primary and secondary evaluation indicators.
Interventions
DRUGChang Gung ECG Abnormality Detection Software
This device is expected to be used for the static 12-lead ECG to detect whether there are abnormal ECG signals related to diseases and outputs the results.
Sponsors
Chang Gung Memorial Hospital
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Equal or greater than twenty years old. * Static 12-lead electrocardiogram of General Electric MUSE XML format file. * The data comes from the static 12-lead electrocardiogram device of General Electric (model MAC5500). * The electrocardiogram signal is 500 Hz. * The Alternating current (AC) filter of the electrocardiogram signal is 60 Hz. * The resource of original diagnosis was a cardiologist.
Exclusion criteria
* Cases used in the model development process. * Lacks any electrode. * Contain any electrode lacks a segment.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity and Specificity | baseline | The rate of test results that correctly indicate the presence and absence. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the receiver operating characteristic Curve | Baseline | A graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. |
Countries
Taiwan
Outcome results
None listed