Foetal ECG recovery using dynamic neural networks

Camps‐Valls, Gustau; Martínez‐Sober, Marcelino; Soria-Olivas, Emilio; Magdalena‐Benedito, Rafael; Calpe‐Maravilla, Javier; Guerrero, Juan

doi:10.1016/j.artmed.2004.03.005

Cited by 60 publications

(36 citation statements)

References 30 publications

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“…6,16,30,31 These extraneous components, which can reduce the signal to noise ratio (SNR) to 1 or less, decrease the quality of the biological signal to the extent that quantitative and even qualitative measurements become difficult if not impossible to make. Subtle features in the biological signal of interest, whether they are of short time duration or of low amplitude or both, are often masked by both broadband noise and low frequency motion artifact.…”

Section: Introductionmentioning

confidence: 99%

Development of Gradient Descent Adaptive Algorithms to Remove Common Mode Artifact for Improvement of Cardiovascular Signal Quality

Ciaccio

Micheli-Tzanakou

2007

Ann Biomed Eng

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Section: Introductionmentioning

confidence: 99%

Development of Gradient Descent Adaptive Algorithms to Remove Common Mode Artifact for Improvement of Cardiovascular Signal Quality

Ciaccio

Micheli-Tzanakou

2007

Ann Biomed Eng

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“…This is owing to the fact that the signal is measured far away from its source (the mother's heart), and consequently it encounters some non-linear transformation as it travels to the abdominal area. Some non-linear modelling methods have been employed to address this problem, such as Neuron Networks [9][10][11][12][13], but it is often confronted with the problem of bad generalisation capability, the trouble of learning non-convergence and selecting the neuron function or network structure on experiences.…”

Section: Introductionmentioning

confidence: 99%

Adaptive filtering in phase space for foetal electrocardiogram estimation from an abdominal electrocardiogram signal and a thoracic electrocardiogram signal

Wei

Liu

Cheng

2012

IET Signal Process.

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In this study, an adaptive filtering in phase space for foetal electrocardiogram (ECG) extraction from a maternal abdominal ECG signal and a thoracic ECG signal is proposed. The phase space trajectory is reconstructed with one abdominal ECG signal and one thoracic ECG signal from a pregnant women. The non-linear and time-variant relationship between the thoracic ECG signal and maternal ECG component in abdominal signal is built by the adaptive local modelling in phase space, and the maternal ECG component in abdominal ECG signal can be estimated, which is then subtracted from the abdominal ECG signal to obtain a foetal ECG estimate. The local modelling process is carried on the spatial neighbouring points of each point in phase space, not on the temporal neighbouring points of each point in time sequence as usual. This can utilise the geometric information of the periodic ECG signal, and the simpler local relationship represented in phase space makes the estimation of the relationship easier and more accurate. Compared with the classical adaptive filter, the proposed method has shown the improved robustness and fidelity in restoration of the foetal ECG, testing with synthetic and real ECG signals.

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“…Various research efforts have been carried out in the area of FECG and FHR extraction, including subtraction of an averaged pattern [2], matched filtering [3], adaptive filtering [4][5][6], orthogonal basis functions [7], fractals [8], FIR [9], dynamic neural networks [10], temporal structure [11], fuzzy logic [12], frequency tracking [13], polynomial networks [14], and real-time signal processing [15]. The wavelet transform (WT) is another approach that has been proposed for FECGs processing.…”

Section: Introductionmentioning

confidence: 99%

A Method for Fetal Heart Rate Extraction Based on Time-Frequency Analysis

Karvounis

Tsipouras

Fotiadis

et al. 2006

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)

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A three-stage method for fetal heart rate extraction, from abdominal ECG recordings, is proposed. In the first stage the maternal R-peaks and fiducial points (QRS onset and offset) are detected, using time-frequency analysis, and the maternal QRS complexes are eliminated. The second stage locates the positions of the candidate fetal R-peaks, using complex wavelets and pattern matching theory techniques. In the third stage, the fetal R-peaks that overlap with the maternal QRS complexes are found. The method is validated using a dataset of 4 long duration recordings and the obtained results indicate high detection ability of the method (96% accuracy).

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Foetal ECG recovery using dynamic neural networks

Cited by 60 publications

References 30 publications

Development of Gradient Descent Adaptive Algorithms to Remove Common Mode Artifact for Improvement of Cardiovascular Signal Quality

Development of Gradient Descent Adaptive Algorithms to Remove Common Mode Artifact for Improvement of Cardiovascular Signal Quality

Adaptive filtering in phase space for foetal electrocardiogram estimation from an abdominal electrocardiogram signal and a thoracic electrocardiogram signal

A Method for Fetal Heart Rate Extraction Based on Time-Frequency Analysis

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