Myocardial Infarction Clinical Trial
Official title:
A Study to Evaluate Accuracy and Validity of the Chang Gung ECG Abnormality Detection Software
Verified date | May 2023 |
Source | Chang Gung Memorial Hospital |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
"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.
Status | Enrolling by invitation |
Enrollment | 4306 |
Est. completion date | February 28, 2024 |
Est. primary completion date | January 31, 2024 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 20 Years and older |
Eligibility | 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. |
Country | Name | City | State |
---|---|---|---|
Taiwan | Chang Gung memorial hospital | Taoyuan city |
Lead Sponsor | Collaborator |
---|---|
Chang Gung Memorial Hospital |
Taiwan,
Acharya U.R., Fujita H., Lih O.S., Adam M., Tan J.H., Chua C.K. Automated detection of coronary artery disease using different durations of ECG segments with convolutional neu-ral network Knowl.-Based Syst., 132 (sep.15) (2017), pp. 62-71
Bos JM, Attia ZI, Albert DE, Noseworthy PA, Friedman PA, Ackerman MJ. Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram. JAMA Cardiol. 2021 May 1;6(5):532-538. doi: 10.1001/jamacardio.2020.7422. — View Citation
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Malik J, Devecioglu OC, Kiranyaz S, Ince T, Gabbouj M. Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks. IEEE Trans Biomed Eng. 2022 May;69(5):1788-1801. doi: 10.1109/TBME.2021.3135622. Epub 2022 Apr 21. — View Citation
Ribeiro AH, Ribeiro MH, Paixao GMM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MPS, Andersson CR, Macfarlane PW, Meira W Jr, Schon TB, Ribeiro ALP. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020 Apr 9;11(1):1760. doi: 10.1038/s41467-020-15432-4. Erratum In: Nat Commun. 2020 May 1;11(1):2227. — View Citation
U. Rajendra Acharya, Hamido Fujita, Oh Shu Lih, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, Automated detection of arrhythmias using different intervals of tachycardia ECG seg-ments with convolutional neural network, Information Sciences, Volume 405, 2017, Pages 81-90, ISSN 0020-0255
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Sensitivity and Specificity | The rate of test results that correctly indicate the presence and absence. | baseline | |
Secondary | Area Under the receiver operating characteristic Curve | A graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. | Baseline |
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