Deep learning for comprehensive ECG annotation.

ABSTRACT:

Background

Increasing utilization of long-term outpatient ambulatory electrocardiographic (ECG) monitoring continues to drive the need for improved ECG interpretation algorithms.

Objective

The purpose of this study was to describe the BeatLogic® platform for ECG interpretation and to validate the platform using electrophysiologist-adjudicated real-world data and publicly available validation data.

Methods

Deep learning models were trained to perform beat and rhythm detection/classification using ECGs collected with the Preventice BodyGuardian® Heart monitor. Training annotations were created by certified ECG technicians, and validation annotations were adjudicated by a team of board-certified electrophysiologists. Deep learning model classification results were used t

SUBMITTER: Teplitzky BA 

PROVIDER: S-EPMC9247885 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature