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

Cardiac Arrest Clinical Trial

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

Clinical Trial 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.

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT03662802
Study type Observational [Patient Registry]
Source Scripps Health
Contact
Status Completed
Phase
Start date October 1, 2018
Completion date October 1, 2020
View Details
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