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A Detections and Measurements in ECG Signals
Toušek, Vojtěch ; Vítek, Martin (referee) ; Kozumplík, Jiří (advisor)
Automatic detection and delineation of ECG characteristic points is a basic procedure of any analyze of ECG using computer. This detection is a necessary step to simplify the work of cardiologists to evaluate long ECG records. In this thesis is proposed and evaluate a method of detection and delineation in a single-lead ECG using dyadic wavelet transform followed by correction in pseudo-orthogonal lead system taken from standard 12-lead system. The method uses information about position of positive maximum – negative minimum pair to detect ECG characteristic waves. At first the QRS complex is detected and than its morphology (waves Q and S) and the onset and end of the complex. After that the T-wave is detected and delineated within a searching window dependent on QRS position. And last the P-wave is detected and delineated. There are used two types of wavelets in developed method, “haar” and “quadratic spline”. The developed method was evaluated on CSE database. When haar wavelet was used the QRS detector sensitivity was 99.14%. In the work is also evaluated the accuracy of delineation characteristic points. As the P-wave and QRS complex delineation produced quite good results the T-wave end delineator produced relatively big deviations. All deviations are presented in histograms.
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Muscle Noise Filtering in ECG Signal
Novotný, J.
This work deals with muscle noise filtering in ECG signals using wiener filtration and optimization of Wiener filter numerical parameters. The optimization was performed by using the exhaustive search method, which belongs to the brute force methods and seeks the global solution. The Criterial function in the optimization process was the average SNR of filtered signals. The testing was performed on 20 random signals from the CSE database. The testing has shown that the greatest difference between the input SNR and the average output SNR was at the minimum input SNR.
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Delineation of Experimental ECG Records
Bucsuházy, K.
This work deals with QRS complex detection and ECG delineation. The proposed method is based on redundant dyadic discrete wavelet transform. It is designed for experimental electrocardiograms of isolated rabbit hearts and it is evaluated through manually determined references. The obtained results showed good accuracy and detection sensitivity and are comparable with another methods.
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Wavelet Based Feature Extraction for Clustering of Be Stars
Bromová, P. ; Škoda, Petr ; Zendulka, J.
The goal of our work is to create a feature extraction method for classification of Be stars. Be stars are characterized by prominent emission lines in their spectrum. We focus on the automated classification of Be stars based on typical shapes of their emission lines. We aim to design a reduced, specific set of features characterizing and discriminating the shapes of Be lines. In this paper, we present a feature extraction method based on the wavelet transform and its power spectrum. Both the discrete and continuous wavelet transform are used. Different feature vectors are created and compared on clustering of Be stars spectra from the archive of the Astronomical Institute of the Academy of Sciences of the Czech Republic. The clustering is performed using the kmeans algorithm. The results of our method are promising and encouraging to more detailed analysis.
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Wavelet Transform and its Application in the Analysis of Economic and Financial Time Series
Bašta, Milan ; Arlt, Josef (advisor) ; Málek, Jiří (referee) ; Mareš, Milan (referee)
The thesis deals with a brief compilation of the theory of Fourier transform, linear filtration and a triad of wavelet transforms -- the maximal overlap discrete wavelet transform (MODWT), the discrete wavelet transform (DWT) and the continuous wavelet transform (CWT). These transforms are among others applied to the analysis of the time-varying character of variability in the time series, to the detection of events of significant changes of variability, to the removal of noise in the time series (denoising) and to the time-scale analysis of the relationship of two time series. The analyzed time series used in this thesis are the logarithm of the Garman-Klass estimate of the historical volatility, the time series of stock returns and the logarithm of the monthly inflation rate. In some cases artificial time series are analyzed. The procedures and methods introduced in the thesis might be well implemented in the analysis of other economic and financial time series. The contribution of the thesis is a brief and easy-to-use compilation of the wavelet theory and the application of the wavelet transform to such financial and economic time series, where such an analysis tool has never been applied before. New insights into the properties of time series are thus obtained, insights, which might be hardly recovered by traditional means and methods.
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Analysis of turbulent flow over forested terrain
Potužníková, Kateřina ; Sedlák, Pavel ; Šauli, Petra
The aim of the present study is to assess and describe low-frequency oscillations and coherent structures in temporal series of temperature and wind velocity components sampled at three different levels within the forest (z/h = 0.3, 0.5 and 1, where h is the canopy height). The wavelet transform is used as a basic tool for our analysis. The periods of detected structures depend on the temperature gradient in the canopy, and furthermore seem to vary with the different local circulations regimes.
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