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Radana Kahankova - One of the best experts on this subject based on the ideXlab platform.

  • non Adaptive methods for fetal ecg Signal Processing a review and appraisal
    Sensors, 2018
    Co-Authors: Rene Jaros, Radek Martinek, Radana Kahankova
    Abstract:

    Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the Signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram Signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-Adaptive Signal Processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-Adaptive method works well for each type of Signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of Signal Processing and have good accuracy and speed of algorithms.

  • non Adaptive methods of fetal ecg Signal Processing
    Advances in Electrical and Electronic Engineering, 2017
    Co-Authors: Radana Kahankova, Radek Martinek, Rene Jaros, J Jezewski, He Wen, Michal Jezewski, Aleksandra Kawalajanik
    Abstract:

    Abdominal fetal ElectroCardioGrams (fECGs) carry a wealth of information about the fetus including fetal Heart Rate (fHR) and Signal morphology during different stages of pregnancy. Here we report our results on the implementation and evaluation of two non-Adaptive Signal Processing methods suitable for fECG Signal extraction, namely: the Independent Component Analysis (ICA) and the Principal Component Analysis (PCA) Methods. We used the fetal heart rate extracted from fECG Signals (in Beats Per Minute - BPM) and Signal-to-Noise Ratio (SNR) as effective performance evaluation metrics for our applied methods. Our findings demonstrated that given adequate SNR, these methods produced excellent results in accurate determination of fHR. Furthermore, we found out that compared to the PCA Method, the ICA Method produces a lower variance in the detection of the fHR.

  • a phonocardiographic based fiber optic sensor and Adaptive filtering system for noninvasive continuous fetal heart rate monitoring
    Sensors, 2017
    Co-Authors: Radek Martinek, Jan Nedoma, Marcel Fajkus, Radana Kahankova, Jaromir Konecny, Petr Janku, Stanislav Kepak, Petr Bilik, Homer Nazeran
    Abstract:

    This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and Adaptive Signal Processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this Signal model, we then designed, implemented, and verified our Adaptive Signal Processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG Signal peak detection. Common Signal Processing methods such as linear filtering, Signal subtraction, and others could not be used for this purpose as fPCG and mPCG Signals share overlapping frequency spectra. The performance of the Adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV.

K Gerlach - One of the best experts on this subject based on the ideXlab platform.

  • robust Adaptive Signal Processing methods for heterogeneous radar clutter scenarios
    Signal Processing, 2004
    Co-Authors: Muralidhar Rangaswamy, Freeman C Lin, K Gerlach
    Abstract:

    This paper addresses the problem of radar target detection in severely heterogeneous clutter environments. Specifically, we present the performance of the normalized matched filter test in a background of disturbance consisting of clutter having a covariance matrix with known structure and unknown scaling plus background white Gaussian noise. It is shown that when the clutter covariance matrix is low rank, the (LRNMF) test retains invariance with respect to the unknown scaling as well as the background noise level and has an approximately constant false alarm rate (CFAR). Performance of the test depends only upon the number of elements, the number of pulses processed in a coherent Processing interval, and the rank of the clutter covariance matrix. Analytical expressions for calculating the false alarm and detection probabilities are presented. Performance of the method is shown to degrade with increasing clutter rank especially for low false alarm rates. An Adaptive version of the test (LRNAMF) is developed and its performance is studied with simulated data from the KASSPER program. Results pertaining to sample support for subspace estimation, CFAR, and detection performance are presented. Target contamination of training data has a deleterious impact on the performance of the test. Therefore, a technique known as self-censoring reiterative fast maximum likelihood/Adaptive power residue (SCRFML/APR) is developed to treat this problem and its performance is discussed. The SCRFML/APR method is used to estimate the unknown covariance matrix in the presence of outliers. This covariance matrix estimate can then be used in the LRNAMF or any other eigen-based Adaptive Processing technique.

  • robust Adaptive Signal Processing methods for heterogeneous radar clutter scenarios
    IEEE Radar Conference, 2003
    Co-Authors: Muralidhar Rangaswamy, Freeman C Lin, K Gerlach
    Abstract:

    This paper addresses the problem of radar target detection in severely heterogeneous clutter environments. Specifically we present the performance of the normalized matched filter (NMF) test in a background of disturbance consisting of clutter having a covariance matrix with known structure and unknown scaling plus background white Gaussian noise. It is shown that when the clutter covariance matrix is low rank, the NMF test retains invariance with respect to the unknown scaling as well as the background noise level and is approximately CFAR. Performance of the test depends only upon the number of elements, number of pulses processed in a coherent Processing interval and the rank of the clutter covariance matrix. Analytical expressions for calculating the false alarm and detection probabilities are presented. Performance of the method is shown to degrade with increasing clutter rank especially for low false alarm rates. An Adaptive version of the test is developed and its performance is studied with simulated data. A technique known as self censoring reiterative fast maximum likelihood/Adaptive power residue (SCRFML/APR) is presented to overcome the problem of outliers in training data for heterogeneous clutter scenarios.

Radek Martinek - One of the best experts on this subject based on the ideXlab platform.

  • non Adaptive methods for fetal ecg Signal Processing a review and appraisal
    Sensors, 2018
    Co-Authors: Rene Jaros, Radek Martinek, Radana Kahankova
    Abstract:

    Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the Signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram Signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-Adaptive Signal Processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-Adaptive method works well for each type of Signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of Signal Processing and have good accuracy and speed of algorithms.

  • non Adaptive methods of fetal ecg Signal Processing
    Advances in Electrical and Electronic Engineering, 2017
    Co-Authors: Radana Kahankova, Radek Martinek, Rene Jaros, J Jezewski, He Wen, Michal Jezewski, Aleksandra Kawalajanik
    Abstract:

    Abdominal fetal ElectroCardioGrams (fECGs) carry a wealth of information about the fetus including fetal Heart Rate (fHR) and Signal morphology during different stages of pregnancy. Here we report our results on the implementation and evaluation of two non-Adaptive Signal Processing methods suitable for fECG Signal extraction, namely: the Independent Component Analysis (ICA) and the Principal Component Analysis (PCA) Methods. We used the fetal heart rate extracted from fECG Signals (in Beats Per Minute - BPM) and Signal-to-Noise Ratio (SNR) as effective performance evaluation metrics for our applied methods. Our findings demonstrated that given adequate SNR, these methods produced excellent results in accurate determination of fHR. Furthermore, we found out that compared to the PCA Method, the ICA Method produces a lower variance in the detection of the fHR.

  • a phonocardiographic based fiber optic sensor and Adaptive filtering system for noninvasive continuous fetal heart rate monitoring
    Sensors, 2017
    Co-Authors: Radek Martinek, Jan Nedoma, Marcel Fajkus, Radana Kahankova, Jaromir Konecny, Petr Janku, Stanislav Kepak, Petr Bilik, Homer Nazeran
    Abstract:

    This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and Adaptive Signal Processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this Signal model, we then designed, implemented, and verified our Adaptive Signal Processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG Signal peak detection. Common Signal Processing methods such as linear filtering, Signal subtraction, and others could not be used for this purpose as fPCG and mPCG Signals share overlapping frequency spectra. The performance of the Adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV.

M. A. Z. Raja - One of the best experts on this subject based on the ideXlab platform.

  • Fractional-order Adaptive Signal Processing strategies for active noise control systems
    Nonlinear Dynamics, 2016
    Co-Authors: Syed M. Shah, R. Samar, N. M. Khan, M. A. Z. Raja
    Abstract:

    Robust and computationally efficient Adaptive algorithms are required in active noise control systems (ANCS) to cancel out the effects of noise in the presence of secondary path (SP) as the latter makes the identification problem more challenging. To ensure stability in such applications, the step size parameter is kept small, but it results in slow convergence which limits the usefulness of such algorithms in ANCS. In this paper, we propose a novel fractional-order Adaptive filter structures such that the output from the conventional filtered-x least mean square algorithm is passed through a new update equation derived from a cost function based on a posteriori error and optimized using fractional derivatives. The proposed algorithms are designed for the feed-forward configuration of ANCS; the schemes are validated using the performance metrics of mean squared error, mean squared deviation and mean relative modeling error. We consider a number of scenarios where different step sizes and fractional orders have been used for evaluation with input Signals modeled as binary or Gaussian. Simulation results show that the proposed algorithms outperform the conventional counterparts with convergence improvements in the range of 75–80 %, while they offer the same steady- state behavior even for large step sizes, thereby providing better modeling in the presence of SP.

Wu Deng - One of the best experts on this subject based on the ideXlab platform.

  • a novel Adaptive Signal Processing method based on enhanced empirical wavelet transform technology
    Sensors, 2018
    Co-Authors: Huimin Zhao, Shaoyan Zuo, Ming Hou, Wei Liu, Xinhua Yang, Wu Deng
    Abstract:

    Empirical wavelet transform (EWT) is a novel Adaptive Signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minimum length curve method is proposed to realize fault diagnosis of motor bearings. The maximum-minimum length curve method transforms the original vibration Signal spectrum to scale space in order to obtain a set of minimum length curves, and find the maximum length curve value in the set of the minimum length curve values for obtaining the number of the spectrum decomposition intervals. The MSCEWT method is used to decompose the vibration Signal into a series of intrinsic mode functions (IMFs), which are processed by Hilbert transform. Then the frequency of each component is extracted by power spectrum and compared with the theoretical value of motor bearing fault feature frequency in order to determine and obtain fault diagnosis result. In order to verify the effectiveness of the MSCEWT method for fault diagnosis, the actual motor bearing vibration Signals are selected and the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods are selected for comparative analysis in here. The results show that the maximum-minimum length curve method can enhance EWT method and the MSCEWT method can solve the shortcomings of the Fourier spectrum segmentation and can effectively decompose the bearing vibration Signal for obtaining less number of intrinsic mode function (IMF) components than the EMD and EEMD methods. It can effectively extract the fault feature frequency of the motor bearing and realize fault diagnosis. Therefore, the study provides a new method for fault diagnosis of rotating machinery.