A deep learning modular ECG approach for cardiologist assisted adjudication of atrial fibrillation and atrial flutter episodes.

ABSTRACT:

Background

Detection of atrial tachyarrhythmias (ATA) on long-term electrocardiogram (ECG) recordings is a prerequisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep learning (DL) algorithms provide improved performances on resting ECG databases. However, results on long-term Holter recordings are scarce.

Objective

We aimed to build and evaluate a DL modular software using ECG features well known to cardiologists with a user interface that allows cardiologists to adjudicate the results and drive a second DL analysis.

Methods

Using a large (n = 187 recordings, 249,419 one-minute samples), beat-to-beat annotated, two-lead Holter database, we built a DL algorithm with a modular structure mimicking expert ph

SUBMITTER: Fleury Q 

PROVIDER: S-EPMC11721725 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature