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Development of a Novel Convolution Neural Network for Arrhythmia Classification (AI-ECG)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT03662802
Recruitment Status : Completed
First Posted : September 7, 2018
Last Update Posted : November 6, 2020
Information provided by (Responsible Party):
Sanjeev Bhavnani MD, Scripps Clinic

Brief Summary:
Identifying the correct arrhythmia at the time of a clinic event including cardiac arrest is of high priority to patients, healthcare organizations, and to public health. Recent developments in artificial intelligence and machine learning are providing new opportunities to rapidly and accurately diagnose cardiac arrhythmias and for how new mobile health and cardiac telemetry devices are used in patient care. The current investigation aims to validate a new artificial intelligence statistical approach called 'convolution neural network classifier' and its performance to different arrhythmias diagnosed on 12-lead ECGs and single-lead Holter/event monitoring. These arrhythmias include; atrial fibrillation, supraventricular tachycardia, AV-block, asystole, ventricular tachycardia and ventricular fibrillation, and will be benchmarked to the American Heart Association performance criteria (95% one-sided confidence interval of 67-92% based on arrhythmia type). In order to do so, the study approach is to create a large ECG database of de-identified raw ECG data, and to train the neural network on the ECG data in order to improve the diagnostic accuracy.

Condition or disease Intervention/treatment
Arrhythmias, Cardiac Cardiac Arrest Cardiac Arrythmias Other: Neural Network Classifier

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Study Type : Observational [Patient Registry]
Actual Enrollment : 25458 participants
Observational Model: Cohort
Time Perspective: Other
Target Follow-Up Duration: 1 Year
Official Title: Development of a Novel Convolution Neural Network for Arrhythmia Classification for Shockable Cardiac Rhythms
Actual Study Start Date : October 1, 2018
Actual Primary Completion Date : March 1, 2020
Actual Study Completion Date : October 1, 2020

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Arrhythmia

Group/Cohort Intervention/treatment
ECG Data
Coded data including; wavelengths, amplitude, intervals, timing, frequence
Other: Neural Network Classifier
The convolutional neural network is configured to receive an electrocardiogram segment as an input and to generate an output indicative of whether the received electrocardiogram segment represents a cardiac arrhythmia. No specific features of the electrocardiogram are identified to the convolutional neural network, and the received electrocardiogram segment is not filtered, transformed, or processed prior to reception by the algorithm. The algorithm is trained in a similar manner - the electrocardiogram segments are the sole input to the convolutional neural network.

Primary Outcome Measures :
  1. Diagnostic Accuracy [ Time Frame: 1 YEAR ]
    American Heart Association ECG Performance Criteria

Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
Individuals undergoing a 12-lead ECG or Holter/Event monitoring

Inclusion Criteria:

  • All ECG data compiled from 12-lead ECG, single, and multiple lead databases

Exclusion Criteria:

  • None

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT03662802

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United States, California
Scripps Clinic
San Diego, California, United States, 92037
Sponsors and Collaborators
Scripps Clinic
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Principal Investigator: Sanjeev Bhavnani, MD Scripps Clinic Medical Group
Additional Information:

Publications of Results:

Other Publications:
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Responsible Party: Sanjeev Bhavnani MD, Principal Investigator - Healthcare Innovation, Scripps Clinic Identifier: NCT03662802    
Other Study ID Numbers: 027527
First Posted: September 7, 2018    Key Record Dates
Last Update Posted: November 6, 2020
Last Verified: November 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Sanjeev Bhavnani MD, Scripps Clinic:
artificial intelligence
machine learning
neural network
cardiac arrhythmia
Additional relevant MeSH terms:
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Arrhythmias, Cardiac
Heart Diseases
Cardiovascular Diseases
Pathologic Processes