Sensor Array and Electrode Selection for Non-invasive Fetal Electrocardiogram Extraction by Independent Component Analysis

Vrins, Frédéric; Jutten, Christian; Verleysen, Michel

doi:10.1007/978-3-540-30110-3_128

Cited by 22 publications

(23 citation statements)

References 3 publications

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“…(3) ICA is better at recovering specific points on the ECG such as the R-peak, RR interval which is necessary for obtaining the heart rate [12][13][14].…”

Section: Ica For Ecg Analysis: a Competitive Choicementioning

confidence: 99%

RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis

Chawla¹,

Verma²,

Kumar³

2008

International Journal of Cardiology

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“…(3) ICA is better at recovering specific points on the ECG such as the R-peak, RR interval which is necessary for obtaining the heart rate [12][13][14].…”

Section: Ica For Ecg Analysis: a Competitive Choicementioning

confidence: 99%

RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis

Chawla¹,

Verma²,

Kumar³

2008

International Journal of Cardiology

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“…The limitations of second order statistics could significantly be reduced by the application of HOS to ECG, to facilitate accurate interpretation of the spatial sampled signal. However, the application of HOS to finite length and noisy ECG data poses a problem of its own and the use of particular order cumulant can help to investigate this problem [15,16]. One limitation that keeps the standard (3 or 12-lead system) ECG from providing a more comprehensive and transparent description of the electrical state of the heart is that it is a very sparse sampling of a complicated, spatially varied distribution of potential on the body surface.…”

Section: Motivation Of Higher Order Statisticsmentioning

confidence: 98%

Parameterization and R‐Peak Error Estimations of ECG Signals Using Independent Component Analysis

Chawla

2007

Computational and Mathematical Methods in Medicine

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Principal component analysis (PCA) is used to reduce dimensionality of electrocardiogram (ECG) data prior to performing independent component analysis (ICA). A newly developed PCA variance estimator by the author has been applied for detecting true, actual and false peaks of ECG data files. In this paper, it is felt that the ability of ICA is also checked for parameterization of ECG signals, which is necessary at times. Independent components (ICs) of properly parameterized ECG signals are more readily interpretable than the measurements themselves, or their ICs. The original ECG recordings and the samples are corrected by statistical measures to estimate the noise statistics of ECG signals and find the reconstruction errors. The capability of ICA is justified by finding the true, false and actual peaks of around 25 -50, CSE (common standards for electrocardiography) database ECG files. In the present work, joint approximation for diagonalization of the eigen matrices (Jade) algorithm is applied to 3-channel ECG. ICA processing of different cases is dealt with and the R-peak magnitudes of the ECG waveforms before and after applying ICA are found and marked. ICA results obtained indicate that in most of the cases, the percentage error in reconstruction is very small. The developed PCA variance estimator along with the quadratic spline wavelet gave a sensitivity of 97.47% before applying ICA and 98.07% after ICA processing.

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“…Nevertheless, for late pregnancies, it has been observed that the fECG morphology is almost independent of electrode position (Lewis, 2003). A large number of electrodes (more than 30) arranged in a wide belt around the mother's abdomen, also containing some electrodes at the back, has been used in laboratory experiments (Cicinelli et al, 1994;Oostendorp, 1989;Vrins et al, 2004).…”

Section: Electrode Placementmentioning

confidence: 99%

ICA-Based Fetal Monitoring

Martı́n-Clemente¹,

Camargo-Olivares²

2012

Independent Component Analysis for Audio and Biosignal Applications

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Sensor Array and Electrode Selection for Non-invasive Fetal Electrocardiogram Extraction by Independent Component Analysis

Cited by 22 publications

References 3 publications

RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis

RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis

Parameterization and R‐Peak Error Estimations of ECG Signals Using Independent Component Analysis

ICA-Based Fetal Monitoring

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