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Maximization of ECG signals diagnostic yield
Beháňová, Andrea ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This bachelor thesis deals with the maximization of ECG signals diagnostic yield. In the theoretical section we deal with the physiology of the heart, electrocardiography, types of ECG noises. It describes some known methods for the estimation of quality of the ECG signal. The practical section contains two parts. The first one contains a continuous Signal-to-Noise Ratio (SNR). It includes generating artificial ECG signal, artificial myopotentials and implementation of Adaptive Wiener Wiener Filtrate (AWWF). After verification of the correctness of the filter on the artificial data, we started to use real data from MIT-BIH database. The second part involves a segmentation process that divides the ECG signal into three categories: a signal suitable for full analysis, suitable for detection of QRS complexes and a signal unsuitable for further analysis.
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Regularization techniques based on the least squares method
Kubínová, Marie ; Hnětynková, Iveta (advisor)
Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Department: Department of Numerical Mathematics Supervisor: RNDr. Iveta Hnětynková, Ph.D. Abstract: In this thesis we consider a linear inverse problem Ax ≈ b, where A is a linear operator with smoothing property and b represents an observation vector polluted by unknown noise. It was shown in [Hnětynková, Plešinger, Strakoš, 2009] that high-frequency noise reveals during the Golub-Kahan iterative bidiagonalization in the left bidiagonalization vectors. We propose a method that identifies the iteration with maximal noise revealing and reduces a portion of high-frequency noise in the data by subtracting the corresponding (properly scaled) left bidiagonalization vector from b. This method is tested for different types of noise. Further, Hnětynková, Plešinger, and Strakoš provided an estimator of the noise level in the data. We propose a modification of this estimator based on the knowledge of the point of noise revealing. Keywords: ill-posed problems, regularization, Golub-Kahan iterative bidiagonalization, noise revealing, noise estimate, denoising 1
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Regularization techniques based on the least squares method
Kubínová, Marie ; Hnětynková, Iveta (advisor)
Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Department: Department of Numerical Mathematics Supervisor: RNDr. Iveta Hnětynková, Ph.D. Abstract: In this thesis we consider a linear inverse problem Ax ≈ b, where A is a linear operator with smoothing property and b represents an observation vector polluted by unknown noise. It was shown in [Hnětynková, Plešinger, Strakoš, 2009] that high-frequency noise reveals during the Golub-Kahan iterative bidiagonalization in the left bidiagonalization vectors. We propose a method that identifies the iteration with maximal noise revealing and reduces a portion of high-frequency noise in the data by subtracting the corresponding (properly scaled) left bidiagonalization vector from b. This method is tested for different types of noise. Further, Hnětynková, Plešinger, and Strakoš provided an estimator of the noise level in the data. We propose a modification of this estimator based on the knowledge of the point of noise revealing. Keywords: ill-posed problems, regularization, Golub-Kahan iterative bidiagonalization, noise revealing, noise estimate, denoising 1
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Regularizační metody založené na metodách nejmenších čtverců
Michenková, Marie ; Hnětynková, Iveta (advisor) ; Zítko, Jan (referee)
Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Department: Department of Numerical Mathematics Supervisor: RNDr. Iveta Hnětynková, Ph.D. Abstract: In this thesis we consider a linear inverse problem Ax ≈ b, where A is a linear operator with smoothing property and b represents an observation vector polluted by unknown noise. It was shown in [Hnětynková, Plešinger, Strakoš, 2009] that high-frequency noise reveals during the Golub-Kahan iterative bidiagonalization in the left bidiagonalization vectors. We propose a method that identifies the iteration with maximal noise revealing and reduces a portion of high-frequency noise in the data by subtracting the corresponding (properly scaled) left bidiagonalization vector from b. This method is tested for different types of noise. Further, Hnětynková, Plešinger, and Strakoš provided an estimator of the noise level in the data. We propose a modification of this estimator based on the knowledge of the point of noise revealing. Keywords: ill-posed problems, regularization, Golub-Kahan iterative bidiagonalization, noise revealing, noise estimate, denoising 1
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Maximization of ECG signals diagnostic yield
Beháňová, Andrea ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This bachelor thesis deals with the maximization of ECG signals diagnostic yield. In the theoretical section we deal with the physiology of the heart, electrocardiography, types of ECG noises. It describes some known methods for the estimation of quality of the ECG signal. The practical section contains two parts. The first one contains a continuous Signal-to-Noise Ratio (SNR). It includes generating artificial ECG signal, artificial myopotentials and implementation of Adaptive Wiener Wiener Filtrate (AWWF). After verification of the correctness of the filter on the artificial data, we started to use real data from MIT-BIH database. The second part involves a segmentation process that divides the ECG signal into three categories: a signal suitable for full analysis, suitable for detection of QRS complexes and a signal unsuitable for further analysis.
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