dorsal/arxiv
View SchemaDeriving the respiratory sinus arrhythmia from the heartbeat time series using Empirical Mode Decomposition
| Authors | R. Balocchi, D. Menicucci, E. Santarcangelo, L. Sebastiani, A. Gemignani, B. Ghelarducci, M. Varanini |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0310002 |
| URL | https://arxiv.org/abs/q-bio/0310002 |
| DOI | 10.1016/S0960-0779(03)00441-7 |
Abstract
Heart rate variability (HRV) is a well-known phenomenon whose characteristics are of great clinical relevance in pathophysiologic investigations. In particular, respiration is a powerful modulator of HRV contributing to the oscillations at highest frequency. Like almost all natural phenomena, HRV is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis of nonlinear and nonstationary data. We applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing. After each decomposition the mode showing the highest frequency and the corresponding respiratory signal were Hilbert transformed and the instantaneous phases extracted were then compared. The results obtained indicate a synchronization of order 1:1 between the two series proving the existence of phase and frequency coupling between the component associated with breathing and the respiratory signal itself in all subjects.
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"abstract": "Heart rate variability (HRV) is a well-known phenomenon whose characteristics\nare of great clinical relevance in pathophysiologic investigations. In\nparticular, respiration is a powerful modulator of HRV contributing to the\noscillations at highest frequency. Like almost all natural phenomena, HRV is\nthe result of many nonlinearly interacting processes; therefore any linear\nanalysis has the potential risk of underestimating, or even missing, a great\namount of information content. Recently the technique of Empirical Mode\nDecomposition (EMD) has been proposed as a new tool for the analysis of\nnonlinear and nonstationary data. We applied EMD analysis to decompose the\nheartbeat intervals series, derived from one electrocardiographic (ECG) signal\nof 13 subjects, into their components in order to identify the modes associated\nwith breathing. After each decomposition the mode showing the highest frequency\nand the corresponding respiratory signal were Hilbert transformed and the\ninstantaneous phases extracted were then compared. The results obtained\nindicate a synchronization of order 1:1 between the two series proving the\nexistence of phase and frequency coupling between the component associated with\nbreathing and the respiratory signal itself in all subjects.",
"arxiv_id": "q-bio/0310002",
"authors": [
"R. Balocchi",
"D. Menicucci",
"E. Santarcangelo",
"L. Sebastiani",
"A. Gemignani",
"B. Ghelarducci",
"M. Varanini"
],
"categories": [
"q-bio.TO",
"q-bio.QM"
],
"doi": "10.1016/S0960-0779(03)00441-7",
"title": "Deriving the respiratory sinus arrhythmia from the heartbeat time series using Empirical Mode Decomposition",
"url": "https://arxiv.org/abs/q-bio/0310002"
},
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