Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition

Balocchi, R.; Menicucci, Danilo; Santarcangelo, ‪Enrica Laura; Sebastiani, Laura; Gemignani, Angelo; Ghelarducci, Brunello; Varanini, M.

doi:10.1016/s0960-0779(03)00441-7

Cited by 144 publications

(88 citation statements)

References 18 publications

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“…The technique has been used extensively in the analysis of ocean wave data (Hwang et al 1999;Huang et al 2003) as well as in the analysis of polar ice cover . EMD has also been applied in the analysis of seismological data by Zhang et al (2003) and has even been used to diagnose heart rate fluctuations (Balocchi et al 2004).…”

Section: Emd Background and The Problemmentioning

confidence: 99%

Empirical mode decomposition using rational splines: an application to rainfall time series

2008

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Empirical mode decomposition ( EMD), a relatively new form of time-series decomposition, has the feature of not assuming that a time series is linear or stationary, as is implicitly done in Fourier analysis. In natural time series such as records of rainfall, streamflow, temperature, etc., where most variables exhibit nonlinear and nonstationary behaviour, this feature is particularly useful, allowing more meaningful quantification of the proportion of variance in a time series due to fluctuations at different time scales, than previous spectral analysis techniques. However, in its original form, the EMD algorithm relies on cubic spline interpolation of the extrema, which often inflate the variance of the resultant components (intrinsic mode functions) and residual. In this paper, a suggested improvement to the EMD algorithm, using rational splines and flexible treatment of the end conditions, is outlined and the consequent effect on three exemplary annual rainfall time series is assessed as a proof of concept. It is recognized that many more examples are needed for the fine tuning of the ideas before it can be widely recommended and this work is currently underway.

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Section: Emd Background and The Problemmentioning

confidence: 99%

Empirical mode decomposition using rational splines: an application to rainfall time series

2008

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“…The EMD decomposes the signal into IMFs with varying amplitude and frequency. These IMFs are assumed to be correlated to physical or physiological aspects of the signals under analysis [26,36]. More specifically, the EMD algorithm consists of the following steps [1]:…”

Section: Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

Noise Cancellation from Vibrocardiographic Signals Based on the Ensemble Empirical Mode Decomposition

TaebiA¹,

Ha²

2017

JABB

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Vibrocardiographic (VCG) signals are the cardiac vibration measured at the chest surface. These signals can contain useful information for diagnosing cardiac conditions but are often contaminated by noise. Although band-pass and adaptive filters were used for noise removal from similar signals, the utility of ensemble empirical mode decomposition (EEMD) for filtering VCG was not previously investigated. In this study, an EEMD-based filter was proposed and tested. The filtering scheme first decomposed the VCG waveform into a set of intrinsic mode functions (IMF) then utilized the partial sum of IMFs to remove white noise that was added to simulated VCG signals. To measure the filter effectiveness, the normalized root-mean-square error (NRMSE) between the clean (i.e., before adding noise) and filtered signals was calculated for signal-to-noise ratios ranging from 1 to 20 dB. The EEMD-based filter performance was also compared with traditional methods such as Wiener filter. This comparison suggested that EEMDbased filter outperformed the Wiener filter in noise removal from simulated VCG. These results also suggested that EEMD may be utilized for white noise removal from actual VCG signals. Further investigations are warranted to study the relation between IMFs and different types of noise, which can enhance the effectiveness of EEMD-based filters in removing these noise types from actual VCG signals.

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“…The technique has been used extensively in the analysis of ocean wave data Hwang et al, 2003) as well as in the analysis of polar ice cover (Gloersen and Huang, 2003). EMD has also been applied in the analysis of seismological data by Zhang et al (2003) and has even been used to diagnose heart rate fluctuations (Balocchi et al, 2004). The algorithm for 1-D EMD is readily available in the cited literature, so is not repeated here; the 2-D extension is based on the 1-D algorithm and is elaborated in Sect.…”

Section: The Basic Principles Of the Emd In One Dimensionmentioning

confidence: 99%

Empirical Mode Decomposition on the sphere: application to the spatial scales of surface temperature variations

Fauchereau

Pegram

Sinclair

2008

Hydrol. Earth Syst. Sci.

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Abstract. Empirical Mode Decomposition (EMD) is applied here in two dimensions over the sphere to demonstrate its potential as a data-adaptive method of separating the different scales of spatial variability in a geophysical (climatological/meteorological) field. After a brief description of the basics of the EMD in 1 then 2 dimensions, the principles of its application on the sphere are explained, in particular via the use of a zonal equal area partitioning. EMD is first applied to an artificial dataset, demonstrating its capability in extracting the different (known) scales embedded in the field. The decomposition is then applied to a global mean surface temperature dataset, and we show qualitatively that it extracts successively larger scales of temperature variations related, for example, to topographic and large-scale, solar radiation forcing. We propose that EMD can be used as a global dataadaptive filter, which will be useful in analysing geophysical phenomena that arise as the result of forcings at multiple spatial scales.

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Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition

Cited by 144 publications

References 18 publications

Empirical mode decomposition using rational splines: an application to rainfall time series

Empirical mode decomposition using rational splines: an application to rainfall time series

Noise Cancellation from Vibrocardiographic Signals Based on the Ensemble Empirical Mode Decomposition

Empirical Mode Decomposition on the sphere: application to the spatial scales of surface temperature variations

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