Fetal ECG Extraction from Sparse Representation of Multichannel Abdominal Recordings

Tavoosi, Parya; Haghi, Fereshteh; Zarjam, Pega; Azemi, Ghasem

doi:10.1007/s00034-021-01870-y

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Several methodologies have been suggested for the isolation and retrieving of sources via the multichannel mother ECG recordings method or by the single mother abdomen ECG signal method. The multichannel method consists on the separation of fetal ECG (FECG) from numerous abdominal mother ECG (MECG) registrations; Tavoosi et al [10] proposed a combination of two blind sources separation (BSS) algorithms, fast independent component analysis (FastICA) and time-frequency BSS to separate the FECG after estimating the source signals from recorded signals. A deep learning approach has been used in [11] by removing the MECG as the first step, then by denoising the multichannel FECG.…”

Section: Introductionmentioning

confidence: 99%

Generalized recursive algorithm for fetal electrocardiogram isolation from non-invasive maternal electrocardiogram

Kaoula,

Guessoum,

Kazed

2023

IJECE

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Non-invasive maternal electrocardiogram recording is the least unpleasant method to record a weak fetal electrocardiogram signal. The importance of this recording lies in the fact that it reveals crucial information about the fetal health state, especially during the last four weeks of pregnancy. This paper will be concerned with a new adaptive algorithm, namely the generalized recursive algorithm, to isolate and get the fetal electrocardiogram from the abdominal maternal electrocardiogram. This is achieved using a non-invasive method for bi-channel maternal electrocardiogram recordings i.e., with the thoracic maternal electrocardiogram as a reference signal, and the abdominal maternal electrocardiogram as a primary signal. Prior to this procedure, the discrete wavelet transform (DWT) method is applied to the abdominal electrocardiogram signal to clean it from any additive noise and the baseline wandering that is generally present on the raw recordings. The proposed new adaptive filter is shown to deliver improved characteristics through simulations. These simulations were performed on both synthetic and actual signals. This work was compared with the normalized least mean square algorithm.

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Section: Introductionmentioning

confidence: 99%

Generalized recursive algorithm for fetal electrocardiogram isolation from non-invasive maternal electrocardiogram

Kaoula,

Guessoum,

Kazed

2023

IJECE

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“…It is well known that biomedical signals can provide useful information for diagnosis of many diseases [7,14,17]. However, many biomedical signals acquired from physical sensors are inevitably contaminated by noise interference that imposes uncertainty to signals [4,22]. As signal quality is reduced, diagnostic performance may be degraded.…”

Section: Introductionmentioning

confidence: 99%

Biomedical Signal Denoising Via Permutating, Thresholding and Averaging Noise Components Obtained from Hierarchical Multiresolution Analysis-Based Empirical Mode Decomposition

Zhou

Ling

Zhou

2022

Circuits Syst Signal Process

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Biomedical signals are usually contaminated with interfering noise, which may result in misdiagnosis of diseases. Additive white Gaussian noise (AWGN) is a common interfering noise, and much work has been proposed to suppress AWGN. Recently, hierarchical multiresolution analysis-based empirical mode decomposition (EMD) denoising method is proposed and shows potential performance. In order to further improve performance of hierarchical multiresolution analysis-based EMD denoising, this paper combines hierarchical multiresolution analysis-based EMD, thresholding operation and averaging operation together. In particular, EMD is applied to the first intrinsic mode function (IMF) in the first level of decomposition to obtain IMFs in the second level of decomposition. The first IMF in the second level of decomposition is chosen as the noise component. For each realization, this noise component is segmented into various pieces, and these segments are permutated. By summing up this permutated IMF to the rest of IMFs in both the first level of decomposition and the second level of decomposition, new realization of the noisy signal is obtained. Next, for original signal and each realization of newly generated noisy signal, EMD is performed again. IMFs in the first level of decomposition are obtained. Then, consecutive mean squared errors-based criterion is used to classify IMFs in the first level of decomposition into the information group of IMFs or the noise group of IMFs. Next, EMD is applied to IMFs in the noise group in the first level of decomposition and IMFs in the second level of decomposition are obtained. Detrended fluctuation analysis is used to classify IMFs in the second level of decomposition into the information group of IMFs or the noise group of IMFs. After that, thresholding is applied to IMFs in the noise group in the second level of the decomposition to obtain denoised signal. Finally, the above procedures are repeated, and several realizations of denoised signals are obtained. Then, denoised signal obtained by applying thresholding to each realization is averaged together to obtain final denoised signal. The extensive numerical simulations are conducted and the results show that our proposed method outperforms existing EMD-based denoising methods.

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Automated Extraction of Fetal ECG Signal Features Using Twinned Filter and Integrated Methodologies

Breesha,

Vinsley

2023

Circuits Syst Signal Process

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Fetal ECG Extraction from Sparse Representation of Multichannel Abdominal Recordings

Cited by 7 publications

References 31 publications

Generalized recursive algorithm for fetal electrocardiogram isolation from non-invasive maternal electrocardiogram

Generalized recursive algorithm for fetal electrocardiogram isolation from non-invasive maternal electrocardiogram

Biomedical Signal Denoising Via Permutating, Thresholding and Averaging Noise Components Obtained from Hierarchical Multiresolution Analysis-Based Empirical Mode Decomposition

Automated Extraction of Fetal ECG Signal Features Using Twinned Filter and Integrated Methodologies

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