Extraction of Fetal Electrocardiogram Using Adaptive Neuro-Fuzzy Inference Systems

Department of Electrical Engineering, American University of Sharjah, P. O. Box 26666, UAE.
IEEE Transactions on Biomedical Engineering (Impact Factor: 2.35). 02/2007; 54(1):59-68. DOI: 10.1109/TBME.2006.883728
Source: IEEE Xplore


In this paper, we investigate the use of adaptive neuro-fuzzy inference systems (ANFIS) for fetal electrocardiogram (FECG) extraction from two ECG signals recorded at the thoracic and abdominal areas of the mother's skin. The thoracic ECG is assumed to be almost completely maternal (MECG) while the abdominal ECG is considered to be composite as it contains both the mother's and the fetus' ECG signals. The maternal component in the abdominal ECG signal is a nonlinearly transformed version of the MECG. We use an ANFIS network to identify this nonlinear relationship, and to align the MECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the FECG component by subtracting the aligned version of the MECG signal from the abdominal ECG signal. We validate our technique on both real and synthetic ECG signals. Our results demonstrate the effectiveness of the proposed technique in extracting the FECG component from abdominal signals of very low maternal to fetal signal-to-noise ratios. The results also show that the technique is capable of extracting the FECG even when it is totally embedded within the maternal QRS complex.

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    • "Since 1960, many signal-processing techniques have been introduced to improve the quality of the FECG detection with varying average of success456. The most popular techniques include adaptive filters [3], singular-value decomposition (SVD) [7], wavelet transform [8], adaptive Neuro-Fuzzy inference systems to treat the nonlinear relationship between the thoracic ECG and the maternal ECG component in the abdominal ECG signals [5]. Another efficient work was the use of blind source separation (BSS) [9]. "
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    ABSTRACT: Background: The electrocardiogram (ECG) is a diagnostic tool that records the electrical activity of the heart, and depicts it as a series of graph-like tracings, or waves. Being able to interpret these details allows diagnosis of a wide range of heart problems. Fetal electrocardiogram (FECG) extraction has an important impact in medical diagnostics during the mother pregnancy period. Since the observed FECG signals are often mixed with the maternal ECG (MECG) and the noise induced by the movement of electrodes or by mother motion, the separation process of the ECG signal sources from the observed data becomes quite complicated. One of its complexity is when the ECG sources are dependent, thus, in this paper we introduce a new approach of blind source separation (BSS) in the noisy context for both independent and dependent ECG signal source. This approach consist in denoising the observed ECG signals using a bilateral total variation (BTV) filter; then minimizing the Kullbak-Leibler divergence between copula densities to separate the FECG signal from the MECG one. Results: We present simulation results illustrating the performance of our proposed method. We will consider many examples of independent/dependent source component signals. The results will be compared with those of the classical method called independent component analysis (ICA) under the same conditions. The accuracy of source estimation is evaluated through a criterion, called again the signal-to-noise-ratio (SNR). The first experiment shows that our proposed method gives accurate estimation of sources in the standard case of independent components, with performance around 27 dB in term of SNR. In the second experiment, we show the capability of the proposed algorithm to successfully separate two noisy mixtures of dependent source components - with classical criterion devoted to the independent case - fails, and that our method is able to deal with the dependent case with good performance. Conclusions: In this work, we focus specifically on the separation of the ECG signal sources taken from skin two electrodes located on a pregnant woman's body. The ECG separation is interpreted as a noisy linear BSS problem with instantaneous mixtures. Firstly, a denoising step is required to reduce the noise due to motion artifacts using a BTV filter as a very effective one-pass filter for denoising. Then, we use the Kullbak-Leibler divergence between copula densities to separate the fetal heart rate from the mother one, for both independent and dependent cases.
    Full-text · Article · Dec 2015 · Theoretical Biology and Medical Modelling
    • "Another restriction of BSS-based algorithms is that they need multi-channel input; so, they require complex lead structure. Other techniques besides BSS have been introduced for FECG extraction such as adaptive filtering [8] [9], neural networks and adaptive neuro-fuzzy inference systems (ANFIS) [10] [11], wavelets [12], singular value decomposition (SVD) [13] [14], and polynomial networks [15]. Among these methods, the polynomial networks method provides a good quality extracted FECG with a non-iterative very low complexity algorithm [15]. "
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    ABSTRACT: This paper introduces a novel two-tier technique for extracting fetal electrocardiogram (ECG) from a single abdominal record. The proposed method in its first tier extracts an estimate of the maternal ECG by processing the abdominal signal through a savitzky-golay smoothing filter. The estimated maternal ECG is then nonlinearly aligned with the abdominal signal using polynomial networks to extract the fetal ECG signal results on synthetic and real abdominal ECG data show that the proposed method can extract fetal ECG with signal quality comparable or better than that extracted by multichannel based methods.
    No preview · Article · Mar 2015
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    • "ANFIS model has combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which Jang (1993) has presented. The mentioned model has been attained its popularity due to a broad range of useful applications in such diverse areas in recent years as optimization of fishing predictions (Nuno et al., 2005; Noureldin et al., 2007; Kishor et al., 2007; Lee and Gardner, 2006; ¨ Ubeyli and G ¨ uler, 2006; Civicioglu, 2007; Qin and Yang, 2007; Daoming and Jie, 2006; Depari et al., 2006; Assaleh, 2007; Huang et al., 2007). All above works manifest that ANFIS model is considered as a good universal approximation, predictor, interpolator and estimator . "
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    ABSTRACT: In this re search, a numerical procedure is used to solve the Navier -Stokes equation on a submerge hydrofoil and the estimation of hydrofoil performance is don e by an Adaptive Neuro -Fuzzy Inference System (ANFIS) model. A pressure-based implicit technique and a non-orthogonal mesh with collocated finite volume formulation are used to simulate flow around the hydrofoil. The procedure incorporates the k-�e eddy viscosity turbulence model and a Volume of Fluid (VOF) process has been utilized to simulate two-phase fluid (water and air ). In the mentioned met hod, the analyses of thickness and camber effect of hydrofoil, submerge distance (h / c ), and the angle of at tack (AOA) make an impression on the hydrofoil performance. To verify the numerical simulation, a part of the presented results is compared with the published experimental data. This comparison confirms the numerical process. Moreover, the hydrofoil configuration and operating condition are assessed by ANFIS model . Consequently, the results prove that t he ANFIS model can predicate t he hydrofoil performance very well.
    Full-text · Dataset · Nov 2014
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