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PubMed2002

Detection of atrial-flutter and atrial-fibrillation waveforms by fetal magnetocardiogram.

Two cases of fetal tachycardia are reported: atrial flutter and fibrillation. The waveforms from each case were detected by fetal magnetocardiograms (FMCGs) using a 64-channel superconducting quantum interference device (SQUID) system. Because the magnitude of supraventricular arrhythmia signals is very weak, two subtraction methods were used to detect the fetal MCG waveforms: subtraction of the maternal MCG signal, and subtraction of the fetal ORS complex signal. It was found that atrial-flutter waveforms showed a cyclic pattern and that atrial-fibrillation waveforms showed f-waves with a random atrial rhythm. Fast Fourier transform analysis determined the main frequency of the atrial flutter to be about 7Hz, and the frequency distribution of atrial fibrillation consisted of small, broad peaks. To visualise the current pattern, current-arrow maps, which simplify the observation of pseudo-current patterns in fetal hearts, of the averaged atrial flutter and fibrillation waveforms were produced. The map of the atrial flutter had a circular pattern, indicating a re-entry circuit, and the map of the atrial fibrillation indicated one wavelet, which was produced by a micro-re-entry circuit. It is thus concluded that an FMCG can detect supraventricular arrhythmia, which can be characterised by re-entry circuits, in fetuses.

Atrial Fibrillation↗
PubMed2003

Multiwavelet grading of pathological images of prostate.

Histological grading of pathological images is used to determine level of malignancy of cancerous tissues. This is a very important task in prostate cancer prognosis, since it is used for treatment planning. If infection of cancer is not rejected by non-invasive diagnostic techniques like magnetic resonance imaging, computed tomography scan, and ultrasound, then biopsy specimens of tissue are tested. For prostate, biopsied tissue is stained by hematoxyline and eosine method and viewed by pathologists under a microscope to determine its histological grade. Human grading is very subjective due to interobserver and intraobserver variations and in some cases difficult and time-consuming. Thus, an automatic and repeatable technique is needed for grading. Gleason grading system is the most common method for histological grading of prostate tissue samples. According to this system, each cancerous specimen is assigned one of five grades. Although some automatic systems have been developed for analysis of pathological images, Gleason grading has not yet been automated; the goal of this research is to automate it. To this end, we calculate energy and entropy features of multiwavelet coefficients of the image. Then, we select most discriminative features by simulated annealing and use a k-nearest neighbor classifier to classify each image to appropriate grade (class). The leaving-one-out technique is used for error rate estimation. We also obtain the results using features extracted by wavelet packets and co-occurrence matrices and compare them with the multiwavelet method. Experimental results show the superiority of the multiwavelet transforms compared with other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated-row preprocessing and has less sensitivity to noise for second level of decomposition. The first level of decomposition is very sensitive to noise and, thus, should not be used for feature extraction. The best multiwavelet method grades prostate pathological images correctly 97% of the time.

Algorithms↗
PubMedDate not supplied

Hybrid neural systems for pattern recognition in artificial noses.

This work examines the use of Hybrid Intelligent Systems in the pattern recognition system of an artificial nose. The connectionist approaches Multi-Layer Perceptron and Time Delay Neural Networks, and the hybrid approaches Feature-Weighted Detector and Evolving Neural Fuzzy Networks were investigated. A Wavelet Filter is evaluated as a preprocessing method for odor signals. The signals generated by an artificial nose were composed by an array of conducting polymer sensors and exposed to two different odor databases.

Animals↗
PubMed2006

[Classification of surface EMG signal based on wavelet transform with nonlinear scale].

Surface EMG (sEMG) signal is a complex nonlinear, non-stationary signal. In this paper, wavelet transform with nonlinear scale (NWT) is introduced. Due to the gradual shortening of its time-resolution, NWT is good at extracting the precise time-frequency information from sEMG signal. First, every sEMG signal (30 sets are for forearm supination and 30 sets are for forearm pronation) is transformed into intensity distribution (time-frequency distribution) by NWT. And then the feature vector is determined from the characteristic roots which are obtained from the intensity distribution by principle component analysis. At last, the two patterns of sEMG signals are identified by BP neural network. The results show that the accurate classification rate is higher gained by NWT than by two conventional time-frequency distributions. At the same time, the calculating complexity of neural network is decreased greatly.

Electromyography↗
PubMedDate not supplied

[Analysis of intracranial pressure signals using artificial neural networks].

Intracranial pressure (ICP) is influenced by an array of predictable and unpredictable factors. Statistical modelling of this signal has only limited applicability because of the significant load of stochastic components. We tested the efficiency of an alternative approach, based on the methodology of artificial neural networks (ANNs) in the on-line prediction of future values of ICP and in the classification of signal properties. Satisfactory accuracy of forecasting was achieved with the ANNs for a 3-minute prediction horizon, while the prediction quality with autoregressive models of statistical origin was proved unsatisfactory. The results obtained with the ANNs were further improved when signal pre-processing with wavelet transform was employed. Nevertheless, even with the ANN methodology, no sudden breakdowns in the ICP signal (which in this respect might be compared to a "catastrophe") can be forecast with any practical applicability. We therefore applied two ANN algorithms, oriented at classification and discrimination of the global properties of the ICP signal. The neural network was expected to discriminate those sets of signal properties, which were assumed to correspond to certain clinical conditions of the patient. In a "dynamic pattern classification" the network was presented with several sections of ICP records. This was combined with information about the assignment of a given record to one of four arbitrary classes of danger. In this mode no data pre-processing was carried out, in contrast to our second approach, in which the signal was pre-processed with statistical analyses and only these intermediate coefficients were fed to the ANN classifier. The results obtained with both classification methods at their present stage of training were similar and approximated to a 70% rate of judgements consistent with expert scoring. Nevertheless, the method based on the assessment of global parameters of the ICP record seems more promising, because it leaves the possibility of extending the set of training data by information from other diagnostic modalities. The study aims towards the development of a pseudo-intelligent computer expert system, which has would be taught salient links between data extracted from the ICP signal and higher- order data, which contributed to the expert score. Hence the system would be able to make decisions on the basis of a reduced set of input information, available from a standard monitoring modality.

Algorithms↗
PubMed1992

Cellular automaton model of ventricular fibrillation.

A theoretical analysis of ventricular fibrillation and the requirements for fibrillation are performed using a discrete element neighborhood (cellular automaton) model of ventricular conduction. The model is configured as a 2500 element rectangular grid on the surface of a cylinder. It is shown that vulnerability to fibrillation is strongly influenced by excited state duration which primarily determines the nature of the underlying reentry activity. As excited state duration is increased fibrillation changes from "coarse" macroreentrant activity to the more chaotic "fine" fibrillation sustained by multiple wavelets of microreentry. In general, defibrillation is achieved by a stimulus strong enough to depolarize the majority of relative refractory elements. The threshold for defibrillation is increased for the more irregular microreentrant fibrillation.

Computer Simulation↗
PubMed1994

Modeling methodology for vascular input impedance determination and interpretation.

The significance of pulse wave reflections in the pulmonary vascular system is elaborated using a new method to determine the broadband frequency response of input impedance up to frequencies of 100 Hz. A simple data model, based on the signal construct of a wavelet, is used to generalize and reconcile the common approaches to vascular frequency response estimation so that an accurate response can be calculated from physiological waveforms. Input impedance interpretation is accomplished using a structural and functional modeling methodology. To identify internal structural system properties, the methodology of inverse scattering is used to relate observed pulse wave echoes in the frequency response to a longitudinal distribution of reflection sites of anatomic significance. To identify functional interactions with pulmonary vascular wave mechanics, a time series analysis methodology is proposed to describe vascular interactions using a generalized principle of superposition. The methods of determination and interpretation are applied to a sample pressure-flow data set from the pulmonary circulation of a lamb experiencing vascular-ventilatory interaction. The example suggests that the frequency response is consistent with a discrete longitudinal distribution of reflection sites that may be affected by the ventilator.

Animals↗
PubMed2003

Regulation of human cutaneous circulation evaluated by laser Doppler flowmetry, iontophoresis, and spectral analysis: importance of nitric oxide and prostaglandines.

Nitric oxide (NO) and prostaglandines (PGs) are important in regulation of vascular tone and blood flow. Their contribution in human cutaneous circulation is still uncertain. We inhibited NO synthesis by infusing N(G)-monomethyl-L-arginine (L-NMMA) in the brachial artery (16 micromol/min for 5 min) and reversed it by intraarterial infusion of L-arginine (40 micromol/min for 7.5 min). PG synthesis was inhibited by the cyclooxygenase inhibitor aspirin (600 mg over 5 min intravenously). Basal cutaneous perfusion and perfusion responses during iontophoresis with the endothelium-dependent vasodilator acetylcholine (ACh) and the endothelium-independent vasodilator sodium nitroprusside (SNP) were recorded by laser Doppler flowmetry (LDF). We performed wavelet transforms of the measured signals. Mean spectral amplitude within the frequency interval from 0.0095 to 1.6 Hz and mean and normalized amplitudes of five intervals around 1, 0.3, 0.1, 0.04, and 0.01 Hz were analysed. The oscillations with frequencies around 1, 0.3, 0.1, and 0.04 Hz are influenced by the heartbeat, the respiration, the intrinsic myogenic activity of vascular smooth muscle, and the neurogenic activity of the vessel wall, respectively. We have previously shown that the oscillation with a frequency around 0.01 Hz is modulated by the vascular endothelium. L-NMMA reduced mean value of the LDF signal by approximately 20% (P = 0.0067). This reduction was reversed by L-arginine. Mean value of the LDF signals during ACh and SNP iontophoresis did not change after infusion of L-NMMA. Aspirin did not affect mean value of the LDF signal or the LDF signal during ACh or SNP iontophoresis. Before interventions the only significant difference between the effects of ACh and SNP was observed in the frequency around 0.01 Hz, where ACh increased normalized amplitude to a greater extent than SNP. L-NMMA abolished this difference, whereas it reappeared after infusion of L-arginine (P = 0.0084). Aspirin did not affect this difference (P = 0.006). We conclude that basal cutaneous blood flow and the endothelial dependency of the oscillation around 0.01 Hz are partly mediated by NO, but not by endogenous PGs. Other aspects of human cutaneous circulation studied are not regulated by NO or PGs.

Acetylcholine↗
PubMed2004

Extraction of shift invariant wavelet features for classification of images with different sizes.

An effective shift invariant wavelet feature extraction method for classification of images with different sizes is proposed. The feature extraction process involves a normalization followed by an adaptive shift invariant wavelet packet transform. An energy signature is computed for each subband of these invariant wavelet coefficients. A reduced subset of energy signatures is selected as the feature vector for classification of images with different sizes. Experimental results show that the proposed method can achieve high classification accuracy of 98.5 percent and outperforms the other two image classification methods.

Algorithms↗
PubMed2001

Decomposition of event-related brain potentials into multiple functional components using wavelet transform.

Event related brain potential (ERP) waveforms consist of several components extending in time, frequency and topographical space. Therefore, an efficient processing of data which involves the time, frequency and space features of the signal, may facilitate understanding the plausible connections among the functions, the anatomical structures and neurophysiological mechanisms of the brain. Wavelet transform (WT) is a powerful signal processing tool for extracting the ERP components occurring at different time and frequency spots. A technical explanation of WT in ERP processing and its four distinct applications are presented here. The first two applications aim to identify and localize the functional oddball ERP components in terms of certain wavelet coefficients in delta, theta and alpha bands in a topographical recording. The third application performs a similar characterization that involves a three stimulus paradigm. The fourth application is a single sweep ERP processing to detect the P300 in single trials. The last case is an extension of ERP component identification by combining the WT with a source localization technique. The aim is to localize the time-frequency components in three dimensional brain structure instead of the scalp surface. The time-frequency analysis using WT helps isolate and describe sequential and/or overlapping functional processes during ERP generation, and provides a possibility for studying these cognitive processes and following their dynamics in single trials during an experimental session.

Algorithms↗
PubMed2003

Visually lossless threshold determination for microcalcification detection in wavelet compressed mammograms.

The aim of this study was to determine the visually lossless threshold of a wavelet-based compression algorithm in case of microcalcification cluster detection in mammography. The threshold was determined by means of observer performance using a set of digitized mammograms. In addition, the transfer characteristics of the compression algorithm were assessed by means of image-quality parameters using computer-generated test images. The observer performance study was based on rating performed by four independent radiologists, who reviewed 68 mammograms, from the Digital Database for Screening Mammography (DDSM), at six different compression ratios. Receiver operating characteristics (ROC) analysis was performed on observers' responses and the area under ROC curve (A(z)) was calculated at each compression ratio for each observer. The parameters used for assessment of transfer characteristics of the compression algorithm were input/output response, noise, high-contrast response, and low-contrast-detail response. The computer-generated test image, used for this assessment, mimicked mammographic image characteristics (pixel size, pixel depth, and noise) as well as microcalcification characteristics (size and contrast). The ROC analysis for microcalcification cluster detection indicated a threshold at compression ratio 40:1, as Student's t-test shows statistically significant differences in A(z) values (p<0.05) for compression ratios 70:1 and 100:1. Observers' grading of mammogram quality lowers this threshold at 25:1. Low-contrast-detail detectability in the transfer characteristics study indicate a threshold of 35:1, whereas non-perceptibility of image-quality-parameters degradation lowers this threshold to 30:1. The ROC and transfer characteristics analysis provided comparable thresholds, indicating the potential use of the latter in limiting the target range of compression ratios for subsequent observer studies.

Aged↗
PubMed2006

A semi-local paradigm for wavelet denoising.

Wavelet denoising methods have been proven useful for many one- and two-dimensional problems. Most existing methods can in principle be carried over to three-dimensional problems, such as the denoising of volumetric positron emission tomography (PET) images, but they may not be sufficiently flexible in allowing some regions of an image to be denoised more aggressively than others. In this paper, we propose a semi-local paradigm for wavelet denoising. The semi-local paradigm involves the division of an image into suitable blocks, which are then individually denoised. To denoise the blocks, we use our modification of the generalized cross validation (GCV) technique of Jansen and Bultheel to choose thresholding parameters; we also present risk estimators to guide some of the other choices involved in the implementation. Experiments with phantom PET images show that the semi-local paradigm provides superior denoising compared to standard application of the GCV technique. An asymptotic analysis demonstrates that, under some regularity conditions, semi-local denoising is asymptotically consistent on the logarithmic scale. The paper concludes with a discussion on the nature of semi-local denoising and some topics for future research.

Algorithms↗
PubMed2004

Evaluation of the influence of image compression to the automatic discrimination of histological images of skin lesions.

OBJECTIVES: Telemedicine offers the possibility to get the opinion of an expert within a short time. To reduce data transfer via a network image compression is recommended. The disadvantage of error-free compression is a low compression rate. On the other hand an increased compression rate affects the information content of the image. In this study we evaluated the influence of the compression rate on the automatic discrimination of histological skin lesions in dermatopathology. METHODS: To be independent from subjective reviewing by a dermatopathologist we used tissue counter analysis (TCA) for automatic discrimination of skin tissue. TCA is based on the partition of the image into square elements where the features are calculated out of each square element. 40 cases of benign common nevi and 40 cases of malignant melanoma were used as the study set. First TCA was applied to the uncompressed images to check the discrimination power of the method. Then in the next steps the method was applied to the images with successively higher compression rates. For image compression the wavelet compression was used, where the compression rate was determined by neglecting wavelet coefficients with a magnitude below a given threshold. The number of remaining wavelet coefficients was used as criteria for the compression rate. RESULTS: This study shows that TCA allows automated discrimination even at higher compression rates where only 6-18% of the wavelet coefficients are used for image reconstruction. The recognition rate at higher compression is better for malignant melanoma than for benign common nevi. CONCLUSION: The power of automated discrimination is not essential affected by wavelet image compression.

Austria↗
PubMed2006

Low-frequency oscillations of the laser Doppler perfusion signal in human skin.

Spectral analysis of the laser Doppler flow (LDF) signal in the frequency interval from 0.0095-2.0 Hz reveals blood flow oscillations with frequencies around 1.0, 0.3, 0.1, 0.04 and 0.01 Hz. The heartbeat, the respiration, the intrinsic myogenic activity of vascular smooth muscle, the neurogenic activity of the vessel wall and the vascular endothelium influence these oscillations, respectively. The first aim of this study was to investigate if a slow oscillatory component could be detected in the frequency area below 0.0095 Hz of the human cutaneous blood perfusion signal. Unstimulated basal blood skin perfusion and enhanced perfusion during iontophoresis with the endothelium-dependent vasodilator acetylcholine (ACh) and the endothelium-independent vasodilator sodium nitroprusside (SNP) were measured in healthy male volunteers and the wavelet transform was computed. A low-frequency oscillation between 0.005 and 0.0095 Hz was found both during basal conditions and during iontophoresis with ACh and SNP. Iontophoresis with ACh increased the normalized amplitude to a greater extent than SNP (P = 0.001) indicating modulation by the vascular endothelium. To gain further insight into the mechanisms for this endothelium dependency, we inhibited nitric oxide (NO) synthesis with N(G)-monomethyl-L-arginine (L-NMMA) and prostaglandin (PG) synthesis by aspirin. L-NMMA did not affect the increased response to ACh vs. SNP iontophoresis in the 0.005-0.0095-Hz interval (P = 0.006) but abolished the difference in the 0.0095-0.021-Hz interval (P = 0.97). Aspirin did not affect the difference in response to ACh and SNP in either of the two frequency intervals. Thus, other endothelial mechanisms, such as endothelium-derived hyperpolarizing factor (EDHF), might be involved in the regulation of this sixth frequency interval (0.005-0.0095 Hz).

Acetylcholine↗
PubMed2005

Illumination-tolerant face verification of low-bit-rate JPEG2000 wavelet images with advanced correlation filters for handheld devices.

Face recognition on mobile devices, such as personal digital assistants and cell phones, is a big challenge owing to the limited computational resources available to run verifications on the devices themselves. One approach is to transmit the captured face images by use of the cell-phone connection and to run the verification on a remote station. However, owing to limitations in communication bandwidth, it may be necessary to transmit a compressed version of the image. We propose using the image compression standard JPEG2000, which is a wavelet-based compression engine used to compress the face images to low bit rates suitable for transmission over low-bandwidth communication channels. At the receiver end, the face images are reconstructed with a JPEG2000 decoder and are fed into the verification engine. We explore how advanced correlation filters, such as the minimum average correlation energy filter [Appl. Opt. 26, 3633 (1987)] and its variants, perform by using face images captured under different illumination conditions and encoded with different bit rates under the JPEG2000 wavelet-encoding standard. We evaluate the performance of these filters by using illumination variations from the Carnegie Mellon University's Pose, Illumination, and Expression (PIE) face database. We also demonstrate the tolerance of these filters to noisy versions of images with illumination variations.

Algorithms↗
PubMed2006

[An improved motion estimation of medical image series via wavelet transform].

The compression of medical image series is very important in telemedicine. The motion estimation plays a key role in the video sequence compression. In this paper, an improved square-diamond search (SDS) algorithm is proposed for the motion estimation of medical image series. The improved SDS algorithm reduces the number of the searched points. This improved SDS algorithm is used in wavelet transformation field to estimate the motion of medical image series. A simulation experiment for digital subtraction angiography (DSA) is made. The experiment results show that the algorithm accuracy is higher than that of other algorithms in the motion estimation of medical image series.

Algorithms↗
PubMed2002

Efficient coding of natural sounds.

The auditory system encodes sound by decomposing the amplitude signal arriving at the ear into multiple frequency bands whose center frequencies and bandwidths are approximately exponential functions of the distance from the stapes. This organization is thought to result from the adaptation of cochlear mechanisms to the animal's auditory environment. Here we report that several basic auditory nerve fiber tuning properties can be accounted for by adapting a population of filter shapes to encode natural sounds efficiently. The form of the code depends on sound class, resembling a Fourier transformation when optimized for animal vocalizations and a wavelet transformation when optimized for non-biological environmental sounds. Only for the combined set does the optimal code follow scaling characteristics of physiological data. These results suggest that auditory nerve fibers encode a broad set of natural sounds in a manner consistent with information theoretic principles.

Animals↗
PubMed2006

Texture characterization for joint compression and classification based on human perception in the wavelet domain.

Today's multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression. The upcoming MPEG 7 standard also addresses the need for succinct classification and indexing of visual content for efficient retrieval. However, there has been no research that has attempted to exploit the characteristics of the human visual system to perform both compression and classification jointly. One area of HVP that has unexplored potential for joint compression and classification is spatial frequency perception. Spatial frequency content that is perceived by humans can be characterized in terms of three parameters, which are: 1) magnitude; 2) phase; and 3) orientation. While the magnitude of spatial frequency content has been exploited in several existing image compression techniques, the novel contribution of this paper is its focus on the use of phase coherence for joint compression and classification in the wavelet domain. Specifically, this paper describes a human visual system-based method for measuring the degree to which an image contains coherent (perceptible) phase information, and then exploits that information to provide joint compression and classification. Simulation results that demonstrate the efficiency of this method are presented.

Algorithms↗