A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network.

ABSTRACT: Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during sleep and is associated with many other medical conditions, including hypertension, coronary heart disease, and depression. Clinically, the standard for diagnosing OSA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of OSA diagnosis in public health sectors. Therefore, electrocardiogram (ECG)-based methods for OSA detection have been proposed to automate the polysomnography procedure and reduce its discomfort. So far, most of the proposed approaches rely on feature engineering, which calls for advanced expert knowledge and experience. This paper proposes a novel fused-image-based technique that detect

SUBMITTER: Niroshana SMI 

PROVIDER: S-EPMC8075238 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature