Multichannel ECG and Noise Modeling: Application to Maternal and Fetal ECG Signals

Sameni, Reza; Clifford, Gari D.; Jutten, Christian; Shamsollahi, Mohammad Bagher

doi:10.1155/2007/43407

Cited by 120 publications

(120 citation statements)

References 31 publications

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“…Synthetic mECG and fECG used in this study are based on a three-dimensional canonical model of the single dipole vector of the heart, proposed in [27] and inspired by the singlechannel ECG dynamic model presented in [25]. Sampling frequency is set to 500 Hz and signals include 20,000 samples.…”

Section: A Experimental Performance Analysis On Synthetic Datamentioning

confidence: 99%

Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings

Niknazar

Rivet

Jutten

2013

IEEE Trans. Biomed. Eng.

124

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Abstract-In this paper, we present an extended nonlinear Bayesian filtering framework for extracting ECGs from a singlechannel as encountered in the fetal ECG extraction from abdominal sensor. The recorded signals are modeled as the summation of several ECGs. Each of them is described by a nonlinear dynamic model, previously presented for the generation of a highly realistic synthetic ECG. Consequently, each ECG has a corresponding term in this model and can thus be efficiently discriminated even if the waves overlap in time. The parameter sensitivity analysis for different values of noise level, amplitude and heart rate ratios between fetal and maternal ECGs shows its effectiveness for a large set of values of these parameters. This framework is also validated on the extractions of fetal ECG from actual abdominal recordings, as well as of actual twin magnetocardiograms.

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Section: A Experimental Performance Analysis On Synthetic Datamentioning

confidence: 99%

Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings

Niknazar

Rivet

Jutten

2013

IEEE Trans. Biomed. Eng.

124

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“…To measure the performance of the algorithms COMBI, MULTICOMBI, WASOBI and EFI-CA, compared with more traditional algorithms such as JADE, we built a semi synthetic database [24], [25] as are 3-D sources representing the maternal and fetal cardiac components [26], are the volume conduction transfer matrices for the mother and fetus respectively. In this model, the maternal signal is assumed as interference while n is assumed as noises for the fetal signal .…”

Section: Semi-synthetic Databasementioning

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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“…The SOB-M-FOCUSS denotes the M-FOCUSS algorithm with smooth overlapped blocks (SOB). The small-scale benchmark contains some multichannel electrocardiography (ECG) signals that were generated with the algorithm proposed by Sameni et al [85]. The signals, which are shown in Fig.…”

Section: Estimation Of the Regularization Parametermentioning

confidence: 99%

Improved M-FOCUSS Algorithm With Overlapping Blocks for Locally Smooth Sparse Signals

Zdunek

Cichocki

2008

IEEE Trans. Signal Process.

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Abstract-The FOCal Underdetermined System Solver (FO-CUSS) algorithm has already found many applications in signal processing and data analysis, whereas the regularized M-FOCUSS algorithm has been recently proposed by Cotter et al. for finding sparse solutions to an underdetermined system of linear equations with multiple measurement vectors. In this paper, we propose three modifications to the M-FOCUSS algorithm to make it more efficient for sparse and locally smooth solutions. First, motivated by the simultaneously autoregressive (SAR) model, we incorporate an additional weighting (smoothing) matrix into the Tikhonov regularization term. Next, the entire set of measurement vectors is divided into blocks, and the solution is updated sequentially, based on the overlapping of data blocks. The last modification is based on an alternating minimization technique to provide data-driven (simultaneous) estimation of the regularization parameter with the generalized cross-validation (GCV) approach. Finally, the simulation results demonstrating the benefits of the proposed modifications support the analysis.Index Terms-FOCal Underdetermined System Solver (FO-CUSS), generalized cross-validation (GCV), smooth signals, sparse solutions, underdetermined systems.

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Multichannel ECG and Noise Modeling: Application to Maternal and Fetal ECG Signals

Cited by 120 publications

References 31 publications

Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings

Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings

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

Improved M-FOCUSS Algorithm With Overlapping Blocks for Locally Smooth Sparse Signals

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