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

NCT05903313 | Unknown

• 1 other identifier: 202300710A5
• Study Type: observational
• Target: 4,306
• Locations: 1 country
• Sites: 1

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.

Conditions

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; Myocardial Infarction

Outcome Measures

Primary Outcomes (1)

• Sensitivity and Specificity

  The rate of test results that correctly indicate the presence and absence.

  baseline

Secondary Outcomes (1)

• Area Under the receiver operating characteristic Curve

  Baseline

Study Arms (1)

Software diagnosis

Software diagnosis with gold standard of 3 cardiologists' interpretation.

Drug: Chang Gung ECG Abnormality Detection Software

Interventions

Chang Gung ECG Abnormality Detection Software DRUG

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.

Also known as: CGMH-EAD-001

Software diagnosis

Eligibility Criteria

• Age: 20 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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. According to inclusion and exclusion criteria, de-identified static 12-lead ECG data from the database during 2006.01.01\~2019.12.31 was collected, and the length of the ECG was 10 seconds.

You may qualify if:

• 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.

You may not qualify if:

• Cases used in the model development process.
• Lacks any electrode.
• Contain any electrode lacks a segment.

Sponsors & Collaborators

• Chang Gung Memorial Hospital: lead

Study Sites (1)

Chang Gung memorial hospital

Taoyuan, 333, Taiwan

Location

Related Publications (8)

• 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.

  PMID: 34910628 BACKGROUND

• 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

  BACKGROUND

• 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.

  PMID: 33566059 BACKGROUND

• 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.

  PMID: 32273514 BACKGROUND

• 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

  BACKGROUND

• 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.

  PMID: 34650175 BACKGROUND

• 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.

  PMID: 31800031 BACKGROUND

• 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.

  PMID: 32439873 BACKGROUND

MeSH Terms

Conditions

Long QT Syndrome; Tachycardia, Sinus; Atrial Premature Complexes; Ventricular Premature Complexes; Atrial Flutter; Bundle-Branch Block; Hypertrophy, Left Ventricular; Myocardial Infarction

Condition Hierarchy (Ancestors)

Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Cardiac Conduction System Disease; Heart Defects, Congenital; Cardiovascular Abnormalities; Congenital Abnormalities; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Tachycardia, Supraventricular; Tachycardia; Cardiac Complexes, Premature; Heart Block; Cardiomegaly; Hypertrophy; Pathological Conditions, Anatomical; Myocardial Ischemia; Vascular Diseases; Infarction; Ischemia; Necrosis

Study Officials

• Chang-Fu Kuo, MD/Ph.D

  Associate Professor and Director Division of Rheumatology

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 6, 2023
• First Posted: June 15, 2023
• Study Start: October 6, 2023
• Primary Completion: January 31, 2024
• Study Completion: February 28, 2024
• Last Updated: January 11, 2024

Record last verified: 2023-05

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)