Artifact Cancellation from Electrocardiogram by Mixed Wavelet-ICA Filter

Foresta, Fabio La; Mammone, Nadia; Morabito, F.

doi:10.1007/11731177_12

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…13,18,30 Both methods reduce the number of source signals by restricting spectral content and employ the virtual channel technique to extract different components from a single observed channel. 20,31 However, other authors maintain that the adaptive function of EMD provides better elimination of interference and better conservation of the target signal than the wavelet in other bio-engineering applications.…”

Section: Combined Methods Based On Emd and Icamentioning

confidence: 99%

“…It can be seen that this process produces ICs with similar morphology to the ECG signal, something that was not possible with the EMD method. After analyzing the correlation of the different ICs with the ECG signal, 3 components of cardiac origin were identified: IC 9 [k], IC 13 [k] y IC 18 [k] (marked by a circle in the figure), since these show a correlation coefficient of 0.44, 0.25 and 0.35, respectively. The rest of the ICs gave a correlation coefficient below 0.20.…”

Section: Intestinal Motility Indexesmentioning

confidence: 99%

“…where M is the estimated number of ICs (16)(17)(18)(19)(20)(21)(22)(23)(24) and ECG[k] is the recorded ECG signal. Concerning the artifacts, as it has already been stated, when they are present, they produce abrupt variations of the potential recorded on the abdominal surface.…”

Section: Identification Of Interferencesmentioning

confidence: 99%

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Combined Method for Reduction of High Frequency Interferences in Surface Electroenterogram (EEnG)

et al. 2010

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Surface electroenterogram (EEnG) recording is a novel technique for monitoring intestinal motility non-invasively. However, surface EEnG recordings are contaminated by cardiac activity, the respiratory artefact, movement artefacts and other types of interference. The goal of this work is to remove electrocardiogram (ECG) interference and movement artefacts from surface EEnG by means of a combined method of empirical mode decomposition and independent component analysis. For this purpose, 11 recording sessions were conducted on animal models. In order to quantify the effectiveness of the proposed method, several parameters were calculated from each session: signal-to-ECG interference ratio (S/I), energy over 2 Hz (EF2) which quantifies the intestinal motility index of external EEnG recording and the variation of EF2. The proposed method removes both ECG interference and movement artefacts from surface EEnG, obtaining a significantly higher S/I ratio and considerably reducing the non-physiological variation of EF2. Furthermore, after applying the combined method, the correlation coefficient between internal recording EF2 and surface recording EF2 rises significantly. The proposed method could therefore be a useful tool to reduce high frequency interference in EEnG recording and to provide more robust non-invasive intestinal motility indexes.

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Section: Combined Methods Based On Emd and Icamentioning

confidence: 99%

Section: Intestinal Motility Indexesmentioning

confidence: 99%

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Combined Method for Reduction of High Frequency Interferences in Surface Electroenterogram (EEnG)

et al. 2010

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“…In this section we describe the wavelet ICA algorithm, which is used in a variety of contexts, such as medical sciences (e.g., Inuso et al 2007;La Foresta et al 2012;Mammone et al 2012), engineering (e.g., Lin & Zhang 2005), acoustic (e.g., Moussaoui et al 2006;Zhao et al 2006), image denoising (e.g., Karande & Talbar 2014), and astrophysics (e.g., Waldmann 2014).…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Repeatability of Spitzer/Irac Exoplanetary Eclipses With Independent Component Analysis

Morello

Waldmann

Tinetti

2016

ApJ

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The research of effective and reliable detrending methods for Spitzer data is of paramount importance for the characterization of exoplanetary atmospheres. To date, the totality of exoplanetary observations in the mid-and farinfrared, at wavelengths >3 μm, have been taken with Spitzer. In some cases, in past years, repeated observations and multiple reanalyses of the same data sets led to discrepant results, raising questions about the accuracy and reproducibility of such measurements. Morello et al. (2014Morello et al. ( , 2015 proposed a blind-source separation method based on the Independent Component Analysis of pixel time series (pixel-ICA) to analyze InfraRed Array Camera (IRAC) data, obtaining coherent results when applied to repeated transit observations previously debated in the literature. Here we introduce a variant to the pixel-ICA through the use of wavelet transform, wavelet pixel-ICA, which extends its applicability to low-signal-to-noise-ratio cases. We describe the method and discuss the results obtained over 12 eclipses of the exoplanet XO3b observed during the "Warm Spitzer" era in the 4.5 μm band. The final results are reported, in part, also in Ingalls et al. ), which appeared on arXiv while this paper was under review. Based on many statistical tests discussed in Ingalls et al. (2016), the wavelet pixel-ICA method performs as well as or better than other state-of-art methods recently developed by other teams to analyze Spitzer/IRAC data, and, in particular, it appears to be the most repeatable and the most reliable, while reaching the photon noise limit, at least for the particular data set analyzed. Another strength of the ICA approach is its highest objectivity, as it does not use prior information about the instrument systematics, making it a promising method to analyze data from other observatories. The selfconsistency of individual measurements of eclipse depth and phase curve slope over a span of more than three years proves the stability of Warm Spitzer/IRAC photometry within the error bars, at the level of 1 part in 10 4 in stellar flux.

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“…A wavelet analysis is broadly used in modeling non-stationary functional data in that morphological information of data can be captured by a set of wavelet coefficients [19,20,14].…”

Section: Introductionmentioning

confidence: 99%