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

November 4, 2020 updated by: Sanjeev Bhavnani MD, Scripps Clinic

Development of a Novel Convolution Neural Network for Arrhythmia Classification for Shockable Cardiac Rhythms

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

25458

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • California
      • San Diego, California, United States, 92037
        • Scripps Clinic

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • ADULT
  • OLDER_ADULT
  • CHILD

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Individuals undergoing a 12-lead ECG or Holter/Event monitoring

Description

Inclusion Criteria:

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

Exclusion Criteria:

  • None

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic Accuracy
Time Frame: 1 YEAR
American Heart Association ECG Performance Criteria
1 YEAR

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Scripps Clinic

Investigators

  • Principal Investigator: Sanjeev Bhavnani, MD, Scripps Clinic Medical Group

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ACTUAL)

October 1, 2018

Primary Completion (ACTUAL)

March 1, 2020

Study Completion (ACTUAL)

October 1, 2020

Study Registration Dates

First Submitted

September 5, 2018

First Submitted That Met QC Criteria

September 5, 2018

First Posted (ACTUAL)

September 7, 2018

Study Record Updates

Last Update Posted (ACTUAL)

November 6, 2020

Last Update Submitted That Met QC Criteria

November 4, 2020

Last Verified

November 1, 2020

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 027527

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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