Wavelet
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Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.
[1. Aufl.] xii, 440 s. : il.
Článek se věnuje návrhu automatického detektoru vybraných arytmií. Navržený algoritmus využívá spojitou vlnkovou transformaci (CWT) v kombinaci s analýzou její konturové obálky. Vlnková transformace byla využita v detektoru R vln, k rozlišení normálních a abnormálních stahů a pro detekci předčasných atriálních kontrakcí (APC) a předčasných komorových kontrakcí (PVC). Algoritmus byl ověřen rozsáhlým testováním na MIT/BIH databázi. Hledáním lokálních maxim ve vlnkové konturové obálce jsou účinně detekovány R vlny. Celková úspěšnost detekce testovaná na 48 půlhodinových signálech je 99,5 %. Byly otestovány dva typy klasifikace: 1. klasifikace založená na konturové obálce a detekci význačných bodů s celkovou úspěšností 94,6 %, 96,1 % pro sinusový rytmus (SR), 30,4 % pro APC, 71,2 % pro PVC a 2. lokalizace maxima umocněných koeficientů spojité vlnkové transformace v oblasti QRS komplexu k určení PVC mezi SR, blokádou pravého Tawarova raménka (RBBB), APC a dalšími arytmiemi s úzkým QRS komplexem s přesností 96,8 %.
This paper deals with design of an automatic detector for classification of selected cardiac arrhythmias. The proposed algorithms employ the continuous wavelet transform (CWT) combined with an analysis of its contour envelopes. The CWT was used in a detector of R-waves, to distinguish between normal and abnormal beats, and for detection of atrial premature contractions (APCs) and premature ventricular contractions (PVCs). The algorithm was validated by extensive testing on the MIT/BIH database. Searching for a local maximum in wavelet contour envelopes efficiently detects R-peaks. The overall accuracy of its detection tested on 48 half-hour signals is 99.5%. Two types of classifications were tested: 1. classification based on the contour envelope and the detection of significant points with overall accuracy 94.6%, 96.1% for the sinus rhythm (SR), 30.4% APCs, 71.2% PVCs and 2. the localization of maximum of square modulus of CWT coefficients in the area of QRS complex for the determination of PVCs between SR, right bundle branch block (RBBB), APC and other narrow complex arrhythmias with the accuracy 96.8%.
- Klíčová slova
- EKG signál, vlnková transformace, kontury,
- MeSH
- algoritmy MeSH
- automatizované zpracování dat metody přístrojové vybavení MeSH
- diagnóza počítačová metody přístrojové vybavení MeSH
- elektrokardiografie klasifikace přístrojové vybavení MeSH
- financování organizované MeSH
- komorové extrasystoly diagnóza MeSH
- lidé MeSH
- počítačové zpracování signálu přístrojové vybavení MeSH
- reprodukovatelnost výsledků MeSH
- síňové extrasystoly diagnóza MeSH
- srdeční arytmie diagnóza klasifikace MeSH
- Check Tag
- lidé MeSH
This work explores the design and implementation of an algorithm for the classification of magnetic resonance imaging data for computer-aided diagnosis of schizophrenia. Features for classification were first extracted using two morphometric methods: voxel-based morphometry (VBM) and deformation-based morphometry (DBM). These features were then transformed into a wavelet domain using the discrete wavelet transform with various numbers of decomposition levels. The number of features was then reduced by thresholding and subsequent selection by: Fisher's Discrimination Ratio (FDR), Bhattacharyya Distance, and Variances (Var.). A Support Vector Machine with a linear kernel was used for classification. The evaluation strategy was based on leave-one-out cross-validation.
- MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- schizofrenie * MeSH
- support vector machine MeSH
- vlnková analýza MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
We present a novel wavelet-based ECG delineation method with robust classification of P wave and T wave. The work is aimed on an adaptation of the method to long-term experimental electrograms (EGs) measured on isolated rabbit heart and to evaluate the effect of global ischemia in experimental EGs on delineation performance. The algorithm was tested on a set of 263 rabbit EGs with established reference points and on human signals using standard Common Standards for Quantitative Electrocardiography Standard Database (CSEDB). On CSEDB, standard deviation (SD) of measured errors satisfies given criterions in each point and the results are comparable to other published works. In rabbit signals, our QRS detector reached sensitivity of 99.87% and positive predictivity of 99.89% despite an overlay of spectral components of QRS complex, P wave and power line noise. The algorithm shows great performance in suppressing J-point elevation and reached low overall error in both, QRS onset (SD = 2.8 ms) and QRS offset (SD = 4.3 ms) delineation. T wave offset is detected with acceptable error (SD = 12.9 ms) and sensitivity nearly 99%. Variance of the errors during global ischemia remains relatively stable, however more failures in detection of T wave and P wave occur. Due to differences in spectral and timing characteristics parameters of rabbit based algorithm have to be highly adaptable and set more precisely than in human ECG signals to reach acceptable performance.
- MeSH
- algoritmy MeSH
- elektrokardiografie metody MeSH
- ischemie patofyziologie MeSH
- králíci MeSH
- lidé MeSH
- počítačové zpracování signálu * MeSH
- srdce patofyziologie MeSH
- vlnková analýza * MeSH
- zvířata MeSH
- Check Tag
- králíci MeSH
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Pial artery adjustments to changes in blood pressure (BP) may last only seconds in humans. Using a novel method called near-infrared transillumination backscattering sounding (NIR-T/BSS) that allows for the non-invasive measurement of pial artery pulsation (cc-TQ) in humans, we aimed to assess the relationship between spontaneous oscillations in BP and cc-TQ at frequencies between 0.5 Hz and 5 Hz. We hypothesized that analysis of very short data segments would enable the estimation of changes in the cardiac contribution to the BP vs. cc-TQ relationship during very rapid pial artery adjustments to external stimuli. BP and pial artery oscillations during baseline (70s and 10s signals) and the response to maximal breath-hold apnea were studied in eighteen healthy subjects. The cc-TQ was measured using NIR-T/BSS; cerebral blood flow velocity, the pulsatility index and the resistive index were measured using Doppler ultrasound of the left internal carotid artery; heart rate and beat-to-beat systolic and diastolic blood pressure were recorded using a Finometer; end-tidal CO2 was measured using a medical gas analyzer. Wavelet transform analysis was used to assess the relationship between BP and cc-TQ oscillations. The recordings lasting 10s and representing 10 cycles with a frequency of ~1 Hz provided sufficient accuracy with respect to wavelet coherence and wavelet phase coherence values and yielded similar results to those obtained from approximately 70cycles (70s). A slight but significant decrease in wavelet coherence between augmented BP and cc-TQ oscillations was observed by the end of apnea. Wavelet transform analysis can be used to assess the relationship between BP and cc-TQ oscillations at cardiac frequency using signals intervals as short as 10s. Apnea slightly decreases the contribution of cardiac activity to BP and cc-TQ oscillations.
- MeSH
- apnoe patologie MeSH
- arteria carotis interna patologie MeSH
- arterie patologie MeSH
- dospělí MeSH
- elektrokardiografie MeSH
- krevní tlak MeSH
- lidé MeSH
- mladý dospělý MeSH
- mozkový krevní oběh MeSH
- oscilometrie metody MeSH
- pia mater krevní zásobení MeSH
- rychlost toku krve fyziologie MeSH
- srdeční frekvence MeSH
- transiluminace metody MeSH
- ultrasonografie dopplerovská transkraniální MeSH
- vlnková analýza * MeSH
- zadržování dechu MeSH
- zdraví dobrovolníci pro lékařské studie MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter.
- MeSH
- algoritmy * MeSH
- databáze faktografické MeSH
- elektrokardiografie metody MeSH
- lidé MeSH
- poměr signál - šum MeSH
- vlnková analýza * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.
The Bitcoin has emerged as a fascinating phenomenon in the Financial markets. Without any central authority issuing the currency, the Bitcoin has been associated with controversy ever since its popularity, accompanied by increased public interest, reached high levels. Here, we contribute to the discussion by examining the potential drivers of Bitcoin prices, ranging from fundamental sources to speculative and technical ones, and we further study the potential influence of the Chinese market. The evolution of relationships is examined in both time and frequency domains utilizing the continuous wavelets framework, so that we not only comment on the development of the interconnections in time but also distinguish between short-term and long-term connections. We find that the Bitcoin forms a unique asset possessing properties of both a standard financial asset and a speculative one.
- MeSH
- časové faktory MeSH
- ekonomické modely * MeSH
- lidé MeSH
- obchod statistika a číselné údaje MeSH
- vlnková analýza MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
