Development of a Novel Convolution Neural Network for Arrhythmia Classification

Study Overview
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Brief Summary
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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.

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

Conditions
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Arrhythmias, Cardiac, Cardiac Arrest, Cardiac Arrythmias

Intervention / Treatment
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* Other: Neural Network Classifier


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: All ECG data compiled from 12-lead ECG, single, and multiple lead databases Exclusion Criteria: None
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| ECG Data<br>Coded data including; wavelengths, amplitude, intervals, timing, frequence | Other: Neural Network Classifier<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

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

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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artificial intelligence, machine learning, neural network, cardiac arrhythmia, ECG, EKG