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Development of a Novel Convolution Neural Network for Arrhythmia Classification

NCT03662802 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 25458

Last updated 2020-11-06

No results posted yet for this study

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.

Conditions

  • Arrhythmias, Cardiac
  • Cardiac Arrest
  • Cardiac Arrythmias

Interventions

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.

Sponsors & Collaborators

  • Scripps Clinic

    lead OTHER

Principal Investigators

  • Sanjeev Bhavnani, MD · Scripps Clinic Medical Group

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2018-10-01
Primary Completion
2020-03-01
Completion
2020-10-01

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03662802 on ClinicalTrials.gov