We use cookies to ensure that we give you the best experience on our website Learn more

Home

Saved research

Submission

Prospective Analysis of Utility of Signals from an ECG-enabled Stethoscope to Automatically Detect a Low Ejection Fraction using neural networks trained from the standard 12-lead ECG

Submitted

116 Views
1 Downloads
1 Saves

Presentation

thumbnail

Abstract

ECG-enabled stethoscopes (ECG-steth) can acquire single lead ECGs during cardiac auscultation, and may facilitate real-time screening for pathologies not routinely identified during physical examination (eg, arrhythmias). We previously demonstrated an artificial intelligence (AI) algorithm applied to a 12-lead ECG (ECG-12) can identify low ejection fraction (EF) (defined as <35%) with an accuracy of 87%. It is unknown if AI algorithms trained from ECG-12 can be applied to single lead ECGs acquired through devices such as ECG-steth. To demonstrate that an AI algorithm trained using ECG-12 can be applied to ECG-steth for detection of low EF.

Datasets

No datasets are available for this submission.

License

No license information is available for this submission.

Company

Legal

Follow us

© Copyright 2019 Morressier GmbH. All rights reserved.

© Copyright 2019 Morressier GmbH.
All rights reserved.