Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
WaveShrink plays pivotal role in analysis of complex bio signals. Wavelet analysis and WaveShrink are the established techniques of statistical research which have to be blended with the analysis of bio signals to provide a high degree of correlation between the desired signal and the observed signal. The multifaceted shrinkage function will obtain lurid responses to the signal perturbed to a very...
Nevoscope is an optical imaging device to obtain images of skin-lesions through back-scattered diffused light using trans-illumination to determine abnormalities such as skin-cancers. Due to the nature of the process of trans-illumination, images obtained from Nevoscope suffer from a dark-field effect. We proposed a two-step algorithm to restore Nevoscope images under the trans-illumination mode....
We applied the concept of "phase synchronization" from nonlinear dynamics to the complex relationship between intracranial pressure (ICP) and arterial blood pressure (ABP) signals. This method is based on multiresolution wavelet transform (MRWT) in which the signals are divided into different frequency bands. We examined ICP and ABP signals from anaesthetized dogs, exploring normal ICP and...
Features of epilepsy from human extracranial EEG recordings were obtained using the wavelet artificial neural network (WANN). The WANN is also a robust signal processing tool for the estimation of nonlinear time-frequency relation and it had previously been shown to be able to classify and predict state transitions in the in vitro hippocampal slice model exhibiting spontaneous epilepsy. The variations...
The objectives of this study is to assess the effect of prolonged loading on the skin blood flowmotion in rats as measured by laser Doppler flowmetry (LDF) using wavelets transform and power spectral in the rat skin microcirculation. External pressure of 13.3kPa (100 mmHg) was applied to the trochanter area and the distal lateral tibia of Sprague-Dawley rats via two specifically designed pneumatic...
A software approach has been developed for detecting electrocautery noise in the electrocardiogram (ECG) using a wavelet decomposition of the signal. With this approach, a clinical monitoring expert system can be forewarned of potential artefacts in trend values derived from the ECG, allowing it to proceed with caution when making decisions based on these trends. In 15 operations spanning 38.5 hours...
Wavelet entropy (WE), a new method of complexity measure for non-stationary signals, was used to investigate the dynamic features of rat EEGs under three vigilance states. The EEGs of the freely moving rats were recorded with implanted electrodes and were decomposed into four components of delta, theta, alpha and beta by using multi-resolution wavelet transform. Then, the wavelet entropy curves were...
In this study, we proposed 17 input features based on wavelet coefficients for arrhythmia detection and, by applying linear discriminant analysis to these, reduced the feature dimension to be 4. Then, with newly constructed 4 dimension input feature, a multi-layer perceptrons classifier was tried to detect 6 types of arrhythmia beats. For evaluation of input features by linear discriminant analysis,...
Electrocardiographic (ECG) analysis plays an important role in safety assessment during new drug development and in clinical diagnosis. The pre-processing of ECG analysis consists of low-frequency baseline wander (BW) correction and high-frequency artifact noise reduction from the raw ECG. We present approaches for BW correction and de-noising based on discrete wavelet transformation (DWT). We estimate...
We proposed an efficient method for classification of diffused liver diseases based on Gabor wavelet. It is well known that Gabor wavelets attain maximum joint space-frequency resolution which is highly significant in the process of texture extraction and presentation. This property has been explored here as the proposed method outperforms the classification rate obtained by using dyadic wavelets...
This paper aimed at building a model for the image registration of CT image and slice image. We used a registration method based on the geometric character of the outline of the image. This method need us do pretreatment to the image, in order to get rid of the noise and enhance the outline of the image, here we use time-frequency theory such as wavelet transformation, gray image fusion etc. The result...
This paper presents a method to segment brain tissue from T1-weighted magnetic resonance (MR) images. A modified BayesShrink method is utilized to filter the image in wavelet transform domain before segmentation, where the shrinkage strength is automatically adjusted with respect to noise level. Then the fuzzy c-means clustering is applied to segment brain tissue into cerebrospinal fluid, gray matter...
Deblurring in the presence of non-Gaussian noise is a hard problem, specially in ultrasonic and CT images. In this paper, a new method of image restoration, using complex wavelet transform, has been devised and applied to deblur in the presence of high speckle noise. It has been shown that the new method outperforms the Weiner filtering and Fourier-wavelet regularized deconvolution (ForWaRD) methods...
How to effectively remove the magnetic resonance imaging (MRI) artifacts in the electroencephalography (EEG) recordings, induced when EEG and functional magnetic resonance imaging (FMRI) are simultaneously recorded, is a challenge for integration of EEG and FMRI. According to the temporal-spatial difference between MRI artifacts and EEG, a new method based on sparse component decomposition in the...
This paper introduces the use of a new tool based on Morlet wavelet transform to detect the interaction dynamics between two neuronal population oscillations. This toolbox can describe the power spectrum, cross wavelet transform, coherence, bi-coherence, cross phase angle and phase synchronization of two neuronal oscillations. Through a case study of focus epilepsy model, a linear and nonlinear correlation...
The application of a recently proposed denoising implementation for obtaining cognitive evoked potentials (CEPs) at the single-trial level is shown. The aim of this investigation is to develop the technique of extracting CEPs by combining both the third-order correlation and the wavelet denoising methods. First, the noisy CEPs was passed through a finite impulse response filter whose impulse response...
Fourier transform of electroencephalography (EEG) restricts EEG analysis due to its stationary properties with time change. It makes analysis difficult to ascertain the global effects of transient change in ischemia and reperfusion model. This study examines that multi-resolution analysis distinguishes different depths of ischemic insult related to the degree of residual blood flow in animal models...
The objective of this paper is to present a secure distribution method to distribute healthcare records (e.g. video streams and digitized image scans). The availability of prompt and expert medical care can meaningfully improve health care services in understaffed rural and remote areas, sharing of available facilities, and medical records referral. Here, a secure method is developed for distributing...
We describe an ensemble of classifiers based data fusion approach to combine information from two sources, believed to contain complimentary information, for early diagnosis of Alzheimer's disease. Specifically, we use the event related potentials recorded from the Pz and Cz electrodes of the EEG, which are further analyzed using multiresolution wavelet analysis. The proposed data fusion approach...
With the number of the elderly population affected by Alzheimer's disease (AD) rising, the need to find an accurate, inexpensive and non-intrusive procedure that can be made available to community healthcare providers for early diagnosis of Alzheimer's disease is becoming more and more urgent as a major health concern. Several recent studies have looked at analyzing electroencephalogram signals through...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.