A Study to Evaluate Accuracy and Validity of the Chang Gung ECG Abnormality Detection Software

Myocardial Infarction Clinical Trial

Official title:

A Study to Evaluate Accuracy and Validity of the Chang Gung ECG Abnormality Detection Software

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT05903313
• Other study ID #: 202300710A5
• Status: Enrolling by invitation
• Start date: October 6, 2023
• Est. completion date: February 28, 2024

Study information

• Verified date: May 2023
• Source: Chang Gung Memorial Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• 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.

Related Conditions & MeSH terms

• Atrial Flutter
• Atrial Premature Complexes
• Bradycardia
• Bundle-Branch Block
• Heart Block
• Hypertrophy
• Hypertrophy, Left Ventricular
• Left Bundle-Branch Block
• Left Ventricular Hypertrophy
• Long QT Syndrome
• Myocardial Infarction
• Premature Atrial Complexes
• Premature Birth
• Premature Ventricular Complexes
• Right Bundle-Branch Block
• Sinus Bradycardia
• Sinus Tachycardia
• Tachycardia
• Tachycardia, Sinus
• Ventricular Premature Complexes

Intervention

Drug:
• Chang 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.

Locations

Country	Name	City	State
Taiwan	Chang Gung memorial hospital	Taoyuan city

Sponsors (1)

Lead Sponsor	Collaborator
Chang Gung Memorial Hospital

Country where clinical trial is conducted

Taiwan, 

References & Publications (8)

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

Jeong DU, Lim KM. Convolutional neural network for classification of eight types of arrhythmia using 2D time-frequency feature map from standard 12-lead electrocardiogram. Sci Rep. 2021 Oct 14;11(1):20396. doi: 10.1038/s41598-021-99975-6. — View Citation

Kwon JM, Jeon KH, Kim HM, Kim MJ, Lim SM, Kim KH, Song PS, Park J, Choi RK, Oh BH. Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography. Europace. 2020 Mar 1;22(3):412-419. doi: 10.1093/europace/euz324. — View Citation

Makimoto H, Hockmann M, Lin T, Glockner D, Gerguri S, Clasen L, Schmidt J, Assadi-Schmidt A, Bejinariu A, Muller P, Angendohr S, Babady M, Brinkmeyer C, Makimoto A, Kelm M. Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction. Sci Rep. 2020 May 21;10(1):8445. doi: 10.1038/s41598-020-65105-x. — View Citation

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

Outcome

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