Investigation of time, amplitude, and frequency parameters of a direct fetal ECG signal during labor and delivery

Shepovalnikov, R. A.; Nemirko, A. P.; Kalinichenko, A. N.; Abramchenko, V. V.

doi:10.1134/s1054661806010238

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…In the simulation, the strength of the FECG, the MECG and the noise were adjusted such that SNR = SIR + 10dB, and SINR was swept in the range of -35dB to -15dB. Note that in the experiment the SINR range was intentionally made more challenging, since for most raw recordings the SINR varies only from -5dB to -25dB [37]. For each value of the SINR, the simulation was repeated 20 times, each time with different signals and noise.…”

Section: A Effects Of Signal-to-interference-and-noise Ratiomentioning

confidence: 99%

Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning

Zhang

Jung

Makeig

et al. 2013

IEEE Trans. Biomed. Eng.

299

202

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Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.

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Section: A Effects Of Signal-to-interference-and-noise Ratiomentioning

confidence: 99%

Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning

Zhang

Jung

Makeig

et al. 2013

IEEE Trans. Biomed. Eng.

299

202

View full text Add to dashboard Cite

show abstract

“…The challenge is to extract the FECG from the composite signal, considering that the two signals are superimposed spectrally, and that the fetal QRS complex amplitude may be less than 3% from the maternal QRS complex [2].…”

Section: Introductionmentioning

confidence: 99%

Fetal electrocardiogram extraction using hybrid BSS technique: COMBI and MULTICOMBI algorithms

Sarmiento-Álvarez¹,

Millet-Roig²,

González-Salvador³

2014

Iteckne

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Abstract-In this paper, we use two algorithms for obtaining fetal ECG from abdominal ECG. The algorithms are MULTICOM-BI and COMBI, which are a combination of EFICA and WASOBI algorithms. The performance of the algorithms COMBI, MUL-TICOMBI, WASOBI, EFICA and traditional JADE algorithm are compared. A semi synthetic database and two actual databases are used to compare the performance of algorithms using as parameter the signal to error ratio SER. It is found that the COMBI and MULTICOMBI algorithms show better performance than the JADE, EFICA and WASOBI algorithms.Keywords-Abdominal ECG, COMBI, fetal ECG, MULTICOMBI, SER, SIR.Resumen-En este artículo se emplean dos algoritmos para obtener el ECG fetal a partir del ECG abdominal. Los algoritmos son MULTICOMBI and COMBI, los cuales son una combinación de los algoritmos EFICA y WASOBI. SE compara el desempeño de los algoritmos COMBI, MULTICOMBI, WASOBI, EFICA y el tradicional algoritmo de JADE. Para comparar el desempeño de los algoritmos se usa una base de datos semi-sintética y dos bases de datos reales usando como parámetro la relación señal a error SER. Se encuentra que los algoritmos COMBI y MULTICOMBI muestran mejor desempeño que los algoritmos JADE, EFICA y WASOBI.Palabras claves -Abdominal ECG, COMBI, fetal ECG, MULTI-COMBI, SER, SIR.

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“…Therefore, this measure is a suitable candidate for the stopping criterion , in the deflation algorithm. The definition of PM may also be extended to vectorial data (23) which can be used as an overall measure of periodicity for multichannel data. Note that the reduction of or is a necessary, but not a sufficient measure of the filtering performance.…”

Section: B Real Data 1) Evaluation Measuresmentioning

confidence: 99%

“…Fetal ECG (fECG) recorded from the maternal abdomen are heavily contaminated with maternal ECG (mECG), which depending on the gestational age and electrode locations, can be up to twenty times stronger than the fetal components [22], [23]. Independent subspace analysis (ISA) and other ICA-based methods are the most common approaches for fECG extraction.…”

Section: Application In Fetal Ecg Extractionmentioning

confidence: 99%

A Deflation Procedure for Subspace Decomposition

Sameni

Jutten²,

Shamsollahi³

2010

IEEE Trans. Signal Process.

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Abstract-A general deflation framework is described for the separation of a desired signal subspace of arbitrary dimensions from noisy multichannel observations. The method simultaneously uses single and multichannel priors to split the desired and undesired subspaces, even for coplanar (intersecting) subspaces. By appropriate use of signal priors, it can even extract signals from degenerate mixtures of signals and noise recorded from a few number of channels in low SNR scenarios, without the reduction of the data dimensions. As a case study, the performance of the proposed method is studied for the problem of extracting fetal cardiac signals from maternal abdominal recordings, over simulated and real data. A second case study deals with the degenerate problem of extracting diaphragmatic electromyogram from electrocardiograph artifacts. A provisional patent application based on this method has been filed.

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Investigation of time, amplitude, and frequency parameters of a direct fetal ECG signal during labor and delivery

Cited by 12 publications

References 5 publications

Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning

Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning

Fetal electrocardiogram extraction using hybrid BSS technique: COMBI and MULTICOMBI algorithms

A Deflation Procedure for Subspace Decomposition

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