Online Algorithm for Deriving Heart Rate Variability Components and Their Time–Frequency Analysis
Heart rate variability (HRV) containing four components of high (HF), low (LF), very low (VLF), and ultra-low (ULF) frequencies provides insight into the cardiovascular and autonomic nervous system functions. Classical spectral analysis is most often used in research on HRV and its components. The a...
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MDPI AG
2025-01-01
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| author | Krzysztof Adamczyk Adam G. Polak |
| author_facet | Krzysztof Adamczyk Adam G. Polak |
| author_sort | Krzysztof Adamczyk |
| collection | DOAJ |
| description | Heart rate variability (HRV) containing four components of high (HF), low (LF), very low (VLF), and ultra-low (ULF) frequencies provides insight into the cardiovascular and autonomic nervous system functions. Classical spectral analysis is most often used in research on HRV and its components. The aim of this work was to develop and validate an online HRV decomposition algorithm for monitoring the associated physiological processes. The online algorithm was developed based on variational mode decomposition (VMD), validated on synthetic HRV with known properties and compared with its offline adaptive version AVMD, standard VMD, continuous wavelet transform (CWT), and wavelet package decomposition (WPD). Finally, it was used to decompose 36 real all-night HRVs from two datasets to analyze the properties of the four extracted components using the Hilbert transform. The statistical tests confirmed that the online VMD (VMDon) algorithm returned results of comparable quality to AVMD and CWT, and outperformed standard VMD and WPD. VMDon, AVMD, and CWT extracted four components from the real HRV with frequency content slightly exceeding the previously recognized ranges, suggesting the possibility of their modes mixing. Their ranges of variability were assessed as follows: HF: 0.11–0.40 Hz; LF: 0.029–0.14 Hz; VLF: 4.7–31 mHz; and ULF: 0.002–3.0 mHz. |
| format | Article |
| id | doaj-art-df2b5dfb15504b79acb34de29a77f851 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-df2b5dfb15504b79acb34de29a77f8512025-08-20T02:48:09ZengMDPI AGApplied Sciences2076-34172025-01-01153121010.3390/app15031210Online Algorithm for Deriving Heart Rate Variability Components and Their Time–Frequency AnalysisKrzysztof Adamczyk0Adam G. Polak1Faculty of Electronics, Photonics and Microsystems, Wrocław University of Science and Technology, 50-372 Wrocław, PolandFaculty of Electronics, Photonics and Microsystems, Wrocław University of Science and Technology, 50-372 Wrocław, PolandHeart rate variability (HRV) containing four components of high (HF), low (LF), very low (VLF), and ultra-low (ULF) frequencies provides insight into the cardiovascular and autonomic nervous system functions. Classical spectral analysis is most often used in research on HRV and its components. The aim of this work was to develop and validate an online HRV decomposition algorithm for monitoring the associated physiological processes. The online algorithm was developed based on variational mode decomposition (VMD), validated on synthetic HRV with known properties and compared with its offline adaptive version AVMD, standard VMD, continuous wavelet transform (CWT), and wavelet package decomposition (WPD). Finally, it was used to decompose 36 real all-night HRVs from two datasets to analyze the properties of the four extracted components using the Hilbert transform. The statistical tests confirmed that the online VMD (VMDon) algorithm returned results of comparable quality to AVMD and CWT, and outperformed standard VMD and WPD. VMDon, AVMD, and CWT extracted four components from the real HRV with frequency content slightly exceeding the previously recognized ranges, suggesting the possibility of their modes mixing. Their ranges of variability were assessed as follows: HF: 0.11–0.40 Hz; LF: 0.029–0.14 Hz; VLF: 4.7–31 mHz; and ULF: 0.002–3.0 mHz.https://www.mdpi.com/2076-3417/15/3/1210heart rate variabilityvariational mode decompositiononline algorithmamplitude and frequency modulation |
| spellingShingle | Krzysztof Adamczyk Adam G. Polak Online Algorithm for Deriving Heart Rate Variability Components and Their Time–Frequency Analysis Applied Sciences heart rate variability variational mode decomposition online algorithm amplitude and frequency modulation |
| title | Online Algorithm for Deriving Heart Rate Variability Components and Their Time–Frequency Analysis |
| title_full | Online Algorithm for Deriving Heart Rate Variability Components and Their Time–Frequency Analysis |
| title_fullStr | Online Algorithm for Deriving Heart Rate Variability Components and Their Time–Frequency Analysis |
| title_full_unstemmed | Online Algorithm for Deriving Heart Rate Variability Components and Their Time–Frequency Analysis |
| title_short | Online Algorithm for Deriving Heart Rate Variability Components and Their Time–Frequency Analysis |
| title_sort | online algorithm for deriving heart rate variability components and their time frequency analysis |
| topic | heart rate variability variational mode decomposition online algorithm amplitude and frequency modulation |
| url | https://www.mdpi.com/2076-3417/15/3/1210 |
| work_keys_str_mv | AT krzysztofadamczyk onlinealgorithmforderivingheartratevariabilitycomponentsandtheirtimefrequencyanalysis AT adamgpolak onlinealgorithmforderivingheartratevariabilitycomponentsandtheirtimefrequencyanalysis |