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

Niknazar, Mohammad; Rivet, Bertrand; Jutten, Christian

doi:10.1109/tbme.2012.2234456

Cited by 120 publications

(78 citation statements)

References 26 publications

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“…The twin pregnancy problem was relatively rarely discussed, except in Niknazar et al [17] and Taylor et al [65]. In Case 5 of the simulated database, although we do not specifically study in this paper, we mention that the proposed algorithm has the potential to handle even multiple pregnancy problem.…”

Section: Several Clinical Topicsmentioning

confidence: 93%

“…Most algorithms need multiple aECG channels and/or one maternal thoracic ECG (mtECG) signal, or at least one aECG channel and one mtECG; including: blind source separation (BSS) [6][7][8][9], semi-BSS like periodic component analysis (πCA), or πTucker decomposition, which takes the pseudo-periodic structure into account [10][11][12], echo state neural network [13], least mean square (LMS) [14], recursive least square (RLS) [13], and blind adaptive filtering [15], Kalman filter [16][17][18], channel selection approach based on features extracted by different methods, like discrete wavelet transform [19], timeadaptive Wiener-filter like filtering [20], principal component regression [21], phase space embedding [22], to name but a few. On the other hand, fewer algorithms depend on the singlelead aECG signal; e.g., template subtraction (TS) [13,[23][24][25][26], and its variation based on singular value decomposition (SVD) or principal component analysis [27,28], the time-frequency analysis, like wavelet transform, pseudo-smooth Wigner-Ville distribution [29][30][31][32] (in practice, three aECG channels are averaged in [30]), and S-transform [33], sequential total variation [34], adaptive neuro-fuzzy inference system and extended Kalman filter [35], particle swarm optimization and extended Kalman smoother [36] state space reconstruction via lag map [37,38], etc.…”

Section: Introductionmentioning

confidence: 99%

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Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

2017

Front. Appl. Math. Stat.

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The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks. To extract the fetal electrocardiogram (ECG) from a single-lead maternal abdominal ECG, we need to solve both challenges. We propose a novel method to extract the fetal ECG from a single-lead maternal abdominal ECG, without any additional measurement. The algorithm is composed of three components. First, the maternal and fetal heart rates are estimated by the de-shape short time Fourier transform (STFT), which is a recently proposed nonlinear time-frequency analysis technique. The beat tracking technique is the second component which is applied to accurately obtain the maternal and fetal R peaks. The third component consists of establishing the maternal and fetal ECG waveforms by the nonlocal median. The algorithm is tested on two real databases with the annotation provided by experts (adfecgdb database and CinC2013 database) and a simulated database (fecgsym), and provides the state-of-the-art results. We conclude that with the proposed algorithm, the fetal ECG waveform and the fetal heart rate could be accurately obtained from the single-lead maternal abdominal ECG.

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Section: Several Clinical Topicsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

2017

Front. Appl. Math. Stat.

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“…ANC is based on training an adaptive filter to remove the projection of thoracic MECG on AECG recordings [11,12,21]. Therefore, the adaptive filter for abdominal MECG removal and FECG extraction require a reference signal that is morphologically similar to the abdominal MECG waveform [22,23]. The literatures [18,24] show that signal propagation from maternal heart to the abdomen is nonlinear and the morphology of the ECG waveforms (abdominal MECG and thoracic MECG) highly depends on the electrode locations.…”

Section: Introductionmentioning

confidence: 99%

A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording

Zhang

et al. 2017

Sensors

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Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain. Research in the area of non-invasive separations of FECG from abdominal electrocardiograms (AECGs) is in its infancy and several studies are currently focusing on this area. An adaptive noise canceller (ANC) is commonly used for cancelling interference in cases where the reference signal only correlates with an interference signal, and not with a signal of interest. However, results from some existing studies suggest that propagation of electrocardiogram (ECG) signals from the maternal heart to the abdomen is nonlinear, hence the adaptive filter approach may fail if the thoracic and abdominal MECG lack strict waveform similarity. In this study, singular value decomposition (SVD) and smooth window (SW) techniques are combined to build a reference signal in an ANC. This is to avoid the limitation that thoracic MECGs recorded separately must be similar to abdominal MECGs in waveform. Validation of the proposed method with r01 and r07 signals from a public dataset, and a self-recorded private dataset showed that the proposed method achieved F1 scores of 99.61%, 99.28% and 98.58%, respectively for the detection of fetal QRS. Compared with four other single-channel methods, the proposed method also achieved higher accuracy values of 99.22%, 98.57% and 97.21%, respectively. The findings from this study suggest that the proposed method could potentially aid accurate extraction of FECG from MECG recordings in both clinical and commercial applications.

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“…Several FECG extraction approaches have produced interesting results: methods based on blind or semi-blind source separation techniques, independent component analysis ( ICA), [1], [2], principal component analysis (PCA) or singular value decomposition (SVD) [3], [4], [5]; average MECG subtraction [6], [7]; different variants of adaptive filters [8], [9], [10]; wavelet decomposition [11].…”

Section: Introductionmentioning

confidence: 99%

Técnicas BSS híbridas para extracción del ECG fetal usando un marco de referencia para pruebas de esfuerzo de algoritmos de extracción

Sarmiento-Álvarez

Millet-Roig

2017

ITECKNE Innovación E Investigación en Ingeniería

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Resumen− La electrocardiografía fetal no invasiva, usa electrodos superficiales puestos sobre el abdomen materno. El reto consiste en extraer la señal fetal de la mezcla abdominal. La Separación Ciega de Fuentes es una forma de hacerlo empleando ya sea ICA o PCA. Los algoritmos COMBI y MULTICOMBI ofrecen un novedoso esquema para combinar ICA y PCA. En este trabajo, se compara el desempeño de los algoritmos COMBI, MUL-TICOMBI, EFICA, WASOBI y los tradicionales JADE e ICA, usando un marco de referencia estándar para la realización de un análisis comparativo y pruebas regulatorias de algoritmos de extracción del electrocardiograma fetal no invasivo. Se usa la medida F1 que combina los picos detectados falsos negativos y falsos positivos para medir la precisión en la detección del complejo fetal QRS. Se llevan a cabo dos experimentos. Uno para determinar el valor de un parámetro de entrada requerido por los algoritmos WASOBI y COMBI, y otro para comparar el desempeño de los algoritmos empleando la medida F1. En el primer experimento se estableció que el orden AR requerido como entrada para COMBI es 10. En el segundo experimento, se obtuvo la precisión media total en la detección del QRS fetal (considerando todos los eventos no estacionarios). Los mejores desempeños en cada grupo fueron 96.4% para COMBI, 95.8% para EFI-CA, 95.2% para JADEICA, 94.6% para PCA, 93,6% para ICAdef, 92.9% para MULTICOMBI, 92,7 para ICAsym, y 89,7% para WASOBI.Palabras clave− FECG, ICA, PCA, pruebas de estrés.Abstract− The non-invasive foetal ECG makes use of surface electrodes placed onto the maternal abdomen. The challenge is to extract the foetal signal from the abdominal mixture. Blind source separation is one way to do this using either ICA or PCA algorithms. COMBI and MULTICOMBI algorithms offer novel schemes for combining ICA and PCA. In this work, the performance of the algorithms COMBI, MULTICOMBI, EFICA, WASOBI and traditional JADE and ICA algorithms are compared, using a standard framework for benchmarking and regulatory testing of NI-FECG extraction algorithms. We use the F1-measure combining true positive, false negative and false positive detected peak for measure the accuracy of FQRS detection. Two experiments were carried out. One to determine a WASOBI algorithm input parameter, also necessary in COMBI, and other to compare performance using F1 measure. In first experiment it was established that the AR order required as input for COMBI is 10. In second experiment, the overall median FQRS detection accuracies (i.e. considering all no stationary events) are obtained. For the best performing methods in each group were 96.4% for COMBI, 95.8% for EFICA, 95.2% for JADEICA, 94.6% for PCA, 93,6% for ICAdef,, 92.9% for MULTICOMBI, 92,7 for ICAsym, and 89,7% for WASOBI.

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

Cited by 120 publications

References 26 publications

Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording

Técnicas BSS híbridas para extracción del ECG fetal usando un marco de referencia para pruebas de esfuerzo de algoritmos de extracción

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