wavelet transform
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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.
- MeSH
- biomedicínské inženýrství MeSH
- elektroencefalografie MeSH
- epilepsie MeSH
- lidé MeSH
- neuronové sítě MeSH
- programovací jazyk MeSH
- software MeSH
- záchvaty MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- srovnávací studie MeSH
Pancreatic microcirculatory dysfunction emerged as a novel mechanism in the development of hypertension. However, the changes of pancreatic microcirculation profiles in hypertension remain unknown. Pancreatic microcirculatory blood distribution pattern and microvascular vasomotion of spontaneously hypertensive rats (SHRs) and Wistar Kyoto rats (WKYs) were determined by laser Doppler. Wavelet transform analysis was performed to convert micro-hemodynamic signals into time-frequency domains, based on which amplitude spectral scalograms were constructed. The amplitudes of characteristic oscillators were compared between SHRs and WKYs. The expression of eNOS was determined by immunohistochemistry, and plasma nitrite/nitrate levels were measured by Griess reaction. Additionally, endothelin-1, malondialdehyde, superoxide dismutase and interleukin-6 were determined by enzyme-linked immunosorbent assay. SHRs exhibited a lower scale blood distribution pattern with decreased average blood perfusion, frequency and amplitude. Wavelet transform spectral analysis revealed significantly reduced amplitudes of endothelial oscillators. Besides reduced expression of eNOS, the blood microcirculatory chemistry complements micro-hemodynamic profiles as demonstrated by an increase in plasma nitrite/nitrate, endothelin-1, malondialdehyde, interleukin-6 and a decrease of superoxide dismutase in SHRs. Here, we described abnormal pancreatic microcirculation profiles in SHRs, including disarranged blood distribution pattern, impaired microvascular vasomotion and reduced amplitudes of endothelial oscillators.
- MeSH
- endotelin-1 metabolismus MeSH
- hypertenze patofyziologie MeSH
- krevní tlak fyziologie MeSH
- krysa rodu rattus MeSH
- laser doppler flowmetrie metody MeSH
- mikrocirkulace MeSH
- modely nemocí na zvířatech MeSH
- oxid dusnatý metabolismus MeSH
- pankreas krevní zásobení patofyziologie MeSH
- potkani inbrední SHR MeSH
- potkani inbrední WKY MeSH
- vlnková analýza MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- mužské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- srovnávací studie MeSH
BACKGROUND: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers. CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.
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
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
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
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
