Application of spectral characteristics of electrocardiogram signals in sleep apnea
BackgroundElectrocardiogram (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.MethodsTo reduce feature complexity and enhance detection acc...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1636011/full |
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| author | Jiayue Hu Liu Yang Xintong Zhao Haicheng Wei Jing Zhao Miaomiao Li |
| author_facet | Jiayue Hu Liu Yang Xintong Zhao Haicheng Wei Jing Zhao Miaomiao Li |
| author_sort | Jiayue Hu |
| collection | DOAJ |
| description | BackgroundElectrocardiogram (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.MethodsTo 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, 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 independent-sample t-test. These features are classified via a random forest machine learning model.ResultsThe 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.ConclusionThis 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. |
| format | Article |
| id | doaj-art-2ecf2f7a24f84879b9500f3f330a34df |
| institution | Matheson Library |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-2ecf2f7a24f84879b9500f3f330a34df2025-07-16T05:36:44ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-07-011310.3389/fbioe.2025.16360111636011Application of spectral characteristics of electrocardiogram signals in sleep apneaJiayue Hu0Liu Yang1Xintong Zhao2Haicheng Wei3Jing Zhao4Miaomiao Li5School of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, ChinaSchool of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, ChinaSchool of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, ChinaSchool of Medical Technology, North Minzu University, Yinchuan, Ningxia, ChinaSchool of Information Engineering, Ningxia University, Yinchuan, Ningxia, ChinaSchool of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, ChinaBackgroundElectrocardiogram (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.MethodsTo 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, 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 independent-sample t-test. These features are classified via a random forest machine learning model.ResultsThe 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.ConclusionThis 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.https://www.frontiersin.org/articles/10.3389/fbioe.2025.1636011/fullsleep apneaEEMD-ICAspectrum featuresIMFrandom Forest |
| spellingShingle | Jiayue Hu Liu Yang Xintong Zhao Haicheng Wei Jing Zhao Miaomiao Li Application of spectral characteristics of electrocardiogram signals in sleep apnea Frontiers in Bioengineering and Biotechnology sleep apnea EEMD-ICA spectrum features IMF random Forest |
| title | Application of spectral characteristics of electrocardiogram signals in sleep apnea |
| title_full | Application of spectral characteristics of electrocardiogram signals in sleep apnea |
| title_fullStr | Application of spectral characteristics of electrocardiogram signals in sleep apnea |
| title_full_unstemmed | Application of spectral characteristics of electrocardiogram signals in sleep apnea |
| title_short | Application of spectral characteristics of electrocardiogram signals in sleep apnea |
| title_sort | application of spectral characteristics of electrocardiogram signals in sleep apnea |
| topic | sleep apnea EEMD-ICA spectrum features IMF random Forest |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1636011/full |
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