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

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ClinicalTrials.gov Identifier: NCT03662802
Recruitment Status : Not yet recruiting
First Posted : September 7, 2018
Last Update Posted : September 7, 2018
Sponsor:
Information provided by (Responsible Party):
Sanjeev Bhavnani MD, Scripps Clinic

Tracking Information
First Submitted Date September 5, 2018
First Posted Date September 7, 2018
Last Update Posted Date September 7, 2018
Estimated Study Start Date October 2018
Estimated Primary Completion Date October 2019   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: September 5, 2018)
Diagnostic Accuracy [ Time Frame: 1 YEAR ]
American Heart Association ECG Performance Criteria
Original Primary Outcome Measures Same as current
Change History No Changes Posted
Current Secondary Outcome Measures Not Provided
Original Secondary Outcome Measures Not Provided
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title Development of a Novel Convolution Neural Network for Arrhythmia Classification
Official Title Development of a Novel Convolution Neural Network for Arrhythmia Classification: The REVIVE-ECG Validation Trial
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.
Detailed Description Not Provided
Study Type Observational [Patient Registry]
Study Design Observational Model: Cohort
Time Perspective: Other
Target Follow-Up Duration 1 Year
Biospecimen Not Provided
Sampling Method Non-Probability Sample
Study Population Individuals undergoing a 12-lead ECG or Holter/Event monitoring
Condition
  • Arrhythmias, Cardiac
  • Cardiac Arrest
  • Cardiac Arrythmias
Intervention 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.
Study Groups/Cohorts ECG Data
Coded data including; wavelengths, amplitude, intervals, timing, frequence
Intervention: Other: Neural Network Classifier
Publications *

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Not yet recruiting
Estimated Enrollment
 (submitted: September 5, 2018)
2000
Original Estimated Enrollment Same as current
Estimated Study Completion Date October 2019
Estimated Primary Completion Date October 2019   (Final data collection date for primary outcome measure)
Eligibility Criteria

Inclusion Criteria:

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

Exclusion Criteria:

  • None
Sex/Gender
Sexes Eligible for Study: All
Ages Child, Adult, Older Adult
Accepts Healthy Volunteers Yes
Contacts
Contact: Sanjeev Bhavnani, MD 6308028202 bhavnani.sanjeev@scrippshealth.org
Listed Location Countries United States
Removed Location Countries  
 
Administrative Information
NCT Number NCT03662802
Other Study ID Numbers 027527
Has Data Monitoring Committee No
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: No
Responsible Party Sanjeev Bhavnani MD, Scripps Clinic
Study Sponsor Scripps Clinic
Collaborators Not Provided
Investigators
Principal Investigator: Sanjeev Bhavnani, MD Scripps Clinic and Research Institute
PRS Account Scripps Health
Verification Date September 2018