ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1636011
This article is part of the Research TopicIntegration of Next-Generation Technologies with Biosensors for Advanced Diagnostics and Personalized MedicineView all articles
Application of spectral characteristics of electrocardiogram signals in sleep apnea
Provisionally accepted
- 1North Minzu University, Yinchuan, China
- 2Ningxia University, Yinchuan, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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.To reduce feature complexity and enhance detection accuracy, we propose a spectral featurebased 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, three spectral features-maximum instantaneous frequency (femax), instantaneous frequency amplitude (V), and marginal spectrum energy (S)-are quantitatively compared between normal and sleep apnea populations using an independentsample t-test. These features are classified via a random forest machine learning model.The femax and IMF7 components of the reconstructed signal exhibited statistically significant differences (p < 0.001) between normal and sleep apnea subjects. The random forest classifier achieved optimal performance, with 92.9% accuracy, 86.6% specificity, and 100% sensitivity.This study demonstrates that spectral features derived from single-lead ECG signals, combined with EEMD-ICA and time-frequency analysis, offer an efficient and accurate method for sleep apnea detection.
Keywords: sleep apnea1, EEMD-ICA2, spectrum features3, IMF4, Random Forest5
Received: 27 May 2025; Accepted: 07 Jul 2025.
Copyright: © 2025 Hu, Yang, Zhao, Wei, Zhao and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Haicheng Wei, North Minzu University, Yinchuan, China
Jing Zhao, Ningxia University, Yinchuan, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.