Deep Neural Network Algorithm Using the Electrocardiogram for Detection of Obstructive Sleep Apnea.

ABSTRACT:

Background

Although highly prevalent, obstructive sleep apnea (OSA) remains largely underdiagnosed, thus justifying the need for high-performing screening tools.

Objectives

The authors sought to develop a machine learning-powered algorithm to identify OSA from the 12-lead electrocardiogram (ECG), a routine clinical test.

Methods

A retrospective population of 11,299 patients who completed sleep evaluation and underwent 12-lead ECG at Mayo Clinic were included. OSA was defined as an apnea-hypopnea index ≥5. A deep convolutional neural network model was constructed to detect OSA from the ECG (artificial intelligence [AI]-ECG). Predictive performance of the algorithm in the total sample and separately in males and females was evaluated using the receiver-operating charact

SUBMITTER: Covassin N 

PROVIDER: S-EPMC12476109 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature