Application of spectral characteristics of electrocardiogram signals in sleep apnea.

ABSTRACT:

Background

Electrocardiogram (ECG) signals contain cardiopulmonary information that can facilitate sleep apnea detection. Traditional methods rely on extracting numerous ECG features, which is labor-intensive and computationally cumbersome.

Methods

To reduce feature complexity and enhance detection accuracy, we propose a spectral feature-based approach using single-lead ECG signals. First, the ECG signal is preprocessed via ensemble empirical mode decomposition combined with independent component analysis (EEMD-ICA) to identify the most representative intrinsic mode function (IMF) based on the maximum instantaneous frequency in the frequency domain. Next, Hilbert transform-based time-frequency analysis is applied to derive the component's 2D time-frequency spectrum. Finally,

SUBMITTER: Hu J 

PROVIDER: S-EPMC12307459 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature