Article

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: 2.23). 02/2007; 54(1):59-68. DOI: 10.1109/TBME.2006.883728
Source: IEEE Xplore

ABSTRACT 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.

Download full-text

Full-text

Available from: Khaled Assaleh, Sep 01, 2015
1 Follower
 · 
211 Views
 · 
267 Downloads
  • Source
    • "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 . "
    [Show abstract] [Hide abstract]
    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.
  • Source
    • "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 . "
    [Show abstract] [Hide abstract]
    ABSTRACT: In this research, a numerical procedure is used to solve the Navier-Stokes equation on a submerge hydrofoil and the estimation of hydrofoil performance is done 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-epsilon 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 method, the analyses of thickness and camber effect of hydrofoil, submerge distance (h/c), and the angle of attack (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 the ANFIS model can predicate the hydrofoil performance very well.
    Ocean Engineering 02/2013; 59. DOI:10.1016/j.oceaneng.2012.10.015 · 1.34 Impact Factor
  • Source
    • "The objective of ANFIS is to adjust the parameters of a fuzzy system by applying a learning procedure using input-output training data. A combination technique of least square algorithm and back propagation are used for training fuzzy inference system [8]. "
    International Journal of Computer Applications 05/2011; 21(8). DOI:10.5120/2532-3450 · 0.82 Impact Factor
Show more