Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

ABSTRACT: Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11744253 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature