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Two types of ventricular fibrillation in isolated rabbit hearts: importance of excitability and action potential duration restitution.

BACKGROUND: The combined effects of excitability and action potential duration (APD) restitution on wavefront dynamics remain unclear. METHODS AND RESULTS: We used optical mapping techniques to study Langendorff-perfused rabbit hearts. In protocol IA (n=10), D600 at increasing concentrations was infused during ventricular fibrillation (VF). With concentration increased to 0.5 mg/L, fast VF (dominant frequency, 19.1+/-1.8 Hz) was consistently converted to ventricular tachycardia (VT). However, increasing D600 further to 2.5 or 5.0 mg/L converted VT to slow VF (11.9+/-2.3 Hz, P=0.0011). In an additional 4 hearts (protocol IB), tetrodotoxin converted a preexisting VT to slow VF (11.0+/-1.4 Hz). Optical maps show wandering wavelets in fast VF, organized reentry in VT, and spatiotemporal periodicity in slow VF. In protocol II, we determined APD and conduction time(-1) (CT(-1)) restitutions during D600 infusion. CT(-1) was used as an estimate of excitability. At 0.1 mg/L, APD and CT(-1) restitutions were steep and flat, respectively. APD restitution became flattened when D600 increased to 0.5 mg/L, converting fast VF to VT. Further increasing D600 to 2.5 or 5.0 mg/L steepened CT(-1) restitution and widened the range of S(1) pacing cycle lengths over which CT(-1) decreased, converting VT to slow VF. CONCLUSIONS: Two types of VF exist in isolated rabbit hearts. Fast (type I) VF is associated with a steep APD restitution, a flat CT(-1) restitution, and wandering wavelets. Slow (type II) VF is associated with a flat APD restitution, a steep CT(-1) restitution, and spatiotemporal periodicity. Both excitability and APD restitution are important in VF maintenance.

Action Potentials↗

Simultaneous multisite mapping studies during induced atrial fibrillation in the sterile pericarditis model. Insights into the mechanism of its maintenance.

BACKGROUND: Chronic atrial fibrillation (AF) is thought to be due to multiple, simultaneously circulating wavelets. In the canine sterile pericarditis model, the mechanisms of maintenance of AF are not yet understood. METHODS AND RESULTS: During six induced AF episodes in six dogs with sterile pericarditis, 372 unipolar electrograms were recorded simultaneously from an electrode array placed around both atrial free walls, along with 10 to 24 electrodes from the atrial septum, by use of a multiplexing system. Activation maps during 12 consecutive 100-ms windows were analyzed from an episode of sustained AF in each dog (mean duration, 32 +/- 24 minutes). In two dogs, two such activation maps during the same episode of AF were analyzed. During AF, multiple unstable reentrant circuits (mean number, 1.4 +/- 0.1 per 100-ms analysis window) with very short cycle lengths (mean, 111 +/- 8 ms) present primarily in the atrial septum and right atrium were responsible for maintenance of AF. The unstable reentrant circuits frequently disappeared and re-formed. Wave fronts traveling from one atrium to the other and/or from the atrial septum play an important role in re-formation of unstable reentrant wave fronts. CONCLUSIONS: In this model of paroxysmal AF, unstable reentrant circuits of very short cycle length principally involving the atrial septum appear to be critical for maintenance of AF. Some reentrant circuits disappear as others re-form, so that at least one reentrant circuit is always present. Because the atria cannot follow their very short cycle lengths in a 1:1 manner, AF is maintained.

Animals↗

A study on medical image registration by mutual information with pyramid data structure.

The registration method based on mutual information is currently a popular technique for medical image registration, but the computation of the mutual information is complex and the registration speed is slow. In this work, a new slice accumulation pyramid (SAP) data structure was proposed to expedite the registration process. A numerical comparative study between the new data structure and the existing wavelet pyramid (WP) data structure was given, and the results confirmed that the new pyramid data structure was superior to the WP in both the calculation efficiency and the optimizing performance. Finally, SAP was applied to remove the artifacts between CT and MRI data sets, and the results showed the validation of SAP to registration of mulmodality images.

Artifacts↗

Wavelet thresholding with bayesian false discovery rate control.

The false discovery rate (FDR) procedure has become a popular method for handling multiplicity in high-dimensional data. The definition of FDR has a natural Bayesian interpretation; it is the expected proportion of null hypotheses mistakenly rejected given a measure of evidence for their truth. In this article, we propose controlling the positive FDR using a Bayesian approach where the rejection rule is based on the posterior probabilities of the null hypotheses. Correspondence between Bayesian and frequentist measures of evidence in hypothesis testing has been studied in several contexts. Here we extend the comparison to multiple testing with control of the FDR and illustrate the procedure with an application to wavelet thresholding. The problem consists of recovering signal from noisy measurements. This involves extracting wavelet coefficients that result from true signal and can be formulated as a multiple hypotheses-testing problem. We use simulated examples to compare the performance of our approach to the Benjamini and Hochberg (1995, Journal of the Royal Statistical Society, Series B57, 289-300) procedure. We also illustrate the method with nuclear magnetic resonance spectral data from human brain.

Bayes Theorem↗

[Analysis of electrically evoked response (EER) in relation to the central visual pathway of the cat (1). Wave shape of the cat EER].

To analyze the Electrically evoked response (EER) in relation to the central visual pathway, the authors studied the properties of wave patterns and peak latencies of EER in 35 anesthetized adult cats. The cat EER showed two early positive waves on outward current (cornea cathode) stimulus and three or four early positive waves on inward current (cornea anode) stimulus. These waves were recorded within 50 ms after stimulus onset, and were the most consistent components in cat EER. The stimulus threshold for EER showed a less individual variation than amplitude. The difference of stimulus threshold between outward and inward current stimulus was also essentially negligible. The stimulus threshold was higher in early components than in late components. The peak latency of EER became shorter and the amplitude became higher, as the stimulus intensity was increased. However, this tendency was reversed and some wavelets started to appear when the stimulus was extremely strong. The recording using short stimulus duration and bipolar electrodes enabled us to reduce the electrical artifact of EER. These results obtained from cats were compared with those of humans and rabbits.

Animals↗

Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients' speech signal with unilateral vocal fold paralysis.

Unilateral vocal fold paralysis (UVFP) is one of the most severe types of neurogenic laryngeal disorder in which the patients, due to their vocal cords malfunction, are confronted by some serious problems. As the effect of such pathologies would be significantly evident in the reduced quality and feature variation of dysphonic voices, this study is designed to scrutinize the piecewise variation of some specific types of these features, known as energy and entropy, all over the frequency range of pathological speech signals. In order to do so, the wavelet-packet coefficients, in five consecutive levels of decomposition, are used to extract the energy and entropy measures at different spectral sub-bands. As the decomposition procedure leads to a set of high-dimensional feature vectors, genetic algorithm is invoked to search for a group of optimal sub-band indexes for which the extracted features result in the highest recognition rate for pathological and normal subjects' classification. The results of our simulations, using support vector machine classifier, show that the highest recognition rate, for both optimized energy and entropy measures, is achieved at the fifth level of wavelet-packet decomposition. It is also found that entropy feature, with the highest recognition rate of 100% vs. 93.62% for energy, is more prominent in discriminating patients with UVFP from normal subjects. Therefore, entropy feature, in comparison with energy, demonstrates a more efficient description of such pathological voices and provides us a valuable tool for clinical diagnosis of unilateral laryngeal paralysis.

Adolescent↗

Unintended interpersonal co-ordination: "can we march to the beat of our own drum?".

The main purpose of this article was to identify whether an interpersonal motor co-ordination emerges between two participants when they intentionally tried to not co-ordinate their movements between each other. To do this we conjointly examined both the emergence of the motor co-ordination (collective properties) between the two participants, and the maintenance of the individual intrinsic motor properties present in the interpersonal co-ordination. Six pairs of participants performed three situations where they freely moved their right forearm with no instructions (nor restrictions) on frequency or relative phase. The goal of the first two situations was for participants to intentionally co-ordinate (P-on) or not co-ordinate (P-off) their movements between each other. In order to determine his/her individual motor properties, each participant performed this task alone, in a third condition. The cross-wavelet transform (CWT) revealed in the P-off condition the emergence of an unintended co-ordination in the frequency domain. Moreover, random distribution of the relative phase appeared, in this condition, whereas in the P-on condition, we observed a collective organization especially at a relative phase around 0 degrees . Conjointly, the wavelet transform (WT) showed the presence of individual intrinsic motor properties (motor signature) in the three conditions. These results indicate that, when there was information sharing, participants could not avoid (unintentionally) coordinating with someone. This study suggests that, in daily life, interactions with other people lead the individual motor behavior to adapt unintended motor co-ordination.

Adult↗

Automatic identification of biological microorganisms using three-dimensional complex morphology.

We propose automated identification of microorganisms using three-dimensional (3-D) complex morphology. This 3-D complex morphology pattern includes the complex amplitude (magnitude and phase) of computationally reconstructed holographic images at arbitrary depths. Microscope-based single-exposure on-line (SEOL) digital holography records and reconstructs holographic images of the biological microorganisms. The 3-D automatic recognition is processed by segmentation, feature extraction by Gabor-based wavelets, automatic feature vector selection by graph matching, training rules, and a decision process. Graph matching combined with Gabor feature vectors measures the similarity of complex geometrical shapes between a reference microorganism and unknown biological samples. Automatic selection of the training data is proposed to achieve a fully automatic recognition system. Preliminary experimental results are presented for 3-D image recognition of Sphacelaria alga and Tribonema aequale alga.

Algorithms↗

Evaluation and optimization of different spectral estimation methods for EEG signals.

The goal of this work was the evaluation of various spectral estimation methods with regard to their suitability for classifying EEG data. A test environment was implemented in which the algorithms are optimized and evaluated using various artificial and real EEG data. The methods are based on autoregressive approaches, as well as from FFT, wavelet, and matching pursuit-based spectral estimations. The evaluation showed that the quality of the results strongly correlate with the computational effort of the algorithm. The matching pursuit algorithm (MP) was implemented and further optimized since it had the best test result and had good scalability. Even under a sufficiently low runtime, it still gave good results.

Algorithms↗

Designing human m1 muscarinic receptor-targeted hydrophobic eigenmode matched peptides as functional modulators.

A new proprietary de novo peptide design technique generated ten 15-residue peptides targeting and containing the leading nontransmembrane hydrophobic autocorrelation wavelengths, "modes", of the human m(1) muscarinic cholinergic receptor, m(1)AChR. These modes were also shared by the m(4)AChR subtype (but not the m(2), m(3), or m(5) subtypes) and the three-finger snake toxins that pseudoirreversibly bind m(1)AChR. The linear decomposition of the hydrophobically transformed m(1)AChR amino acid sequence yielded ordered eigenvectors of orthogonal hydrophobic variational patterns. The weighted sum of two eigenvectors formed the peptide design template. Amino acids were iteratively assigned to template positions randomly, within hydrophobic groups. One peptide demonstrated significant functional indirect agonist activity, and five produced significant positive allosteric modulation of atropine-reversible, direct-agonist-induced cellular activation in stably m(1)AChR-transfected Chinese hamster ovary cells, reflected in integrated extracellular acidification responses. The peptide positive allosteric ligands produced left-shifts and peptide concentration-response augmentation in integrated extracellular acidification response asymptotic sigmoidal functions and concentration-response behavior in Hill number indices of positive cooperativity. Peptide mode specificity was suggested by negative crossover experiments with human m(2)ACh and D(2) dopamine receptors. Morlet wavelet transformation of the leading eigenvector-derived, m(1)AChR eigenfunctions locates seven hydrophobic transmembrane segments and suggests possible extracellular loop locations for the peptide-receptor mode-matched, modulatory hydrophobic aggregation sites.

Algorithms↗

JPEG2000-coded image error concealment exploiting convex sets projections.

Transmission errors in JPEG2000 can be grouped into three main classes, depending on the affected area: LL, high frequencies at the lower decomposition levels, and high frequencies at the higher decomposition levels. The first type of errors are the most annoying but can be concealed exploiting the signal spatial correlation like in a number of techniques proposed in the past; the second are less annoying but more difficult to address; the latter are often imperceptible. In this paper, we address the problem of concealing the second class or errors when high bit-planes are damaged by proposing a new approach based on the theory of projections onto convex sets. Accordingly, the error effects are masked by iteratively applying two procedures: low-pass (LP) filtering in the spatial domain and restoration of the uncorrupted wavelet coefficients in the transform domain. It has been observed that a uniform LP filtering brought to some undesired side effects that negatively compensated the advantages. This problem has been overcome by applying an adaptive solution, which exploits an edge map to choose the optimal filter mask size. Simulation results demonstrated the efficiency of the proposed approach.

Algorithms↗

Wavelet-based combined signal filtering and prediction.

We survey a number of applications of the wavelet transform in time series prediction. We show how multiresolution prediction can capture short-range and long-term dependencies with only a few parameters to be estimated. We then develop a new multiresolution methodology for combined noise filtering and prediction, based on an approach which is similar to the Kalman filter. Based on considerable experimental assessment, we demonstrate the powerfulness of this methodology.

Algorithms↗

Efficient iris recognition by characterizing key local variations.

Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

Algorithms↗

A 2-D orientation-adaptive prediction filter in lifting structures for image coding.

Lifting-style implementations of wavelets are widely used in image coders. A two-dimensional (2-D) edge adaptive lifting structure, which is similar to Daubechies 5/3 wavelet, is presented. The 2-D prediction filter predicts the value of the next polyphase component according to an edge orientation estimator of the image. Consequently, the prediction domain is allowed to rotate +/-45 degrees in regions with diagonal gradient. The gradient estimator is computationally inexpensive with additional costs of only six subtractions per lifting instruction, and no multiplications are required.

Algorithms↗

Locally adaptive wavelet-based image interpolation.

We describe a spatially adaptive algorithm for image interpolation. The algorithm uses a wavelet transform to extract information about sharp variations in the low-resolution image and then implicitly applies interpolation which adapts to the image local smoothness/singularity characteristics. The proposed algorithm yields images that are sharper compared to several other methods that we have considered in this paper. Better performance comes at the expense of higher complexity.

Algorithms↗

Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism.

An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.

Algorithms↗

Pores and ridges: high-resolution fingerprint matching using level 3 features.

Fingerprint friction ridge details are generally described in a hierarchical order at three different levels, namely, Level 1 (pattern), Level 2 (minutia points), and Level 3 (pores and ridge contours). Although latent print examiners frequently take advantage of Level 3 features to assist in identification, Automated Fingerprint Identification Systems (AFIS) currently rely only on Level 1 and Level 2 features. In fact, the Federal Bureau of Investigation's (FBI) standard of fingerprint resolution for AFIS is 500 pixels per inch (ppi), which is inadequate for capturing Level 3 features, such as pores. With the advances in fingerprint sensing technology, many sensors are now equipped with dual resolution (500 ppi/1,000 ppi) scanning capability. However, increasing the scan resolution alone does not necessarily provide any performance improvement in fingerprint matching, unless an extended feature set is utilized. As a result, a systematic study to determine how much performance gain one can achieve by introducing Level 3 features in AFIS is highly desired. We propose a hierarchical matching system that utilizes features at all the three levels extracted from 1,000 ppi fingerprint scans. Level 3 features, including pores and ridge contours, are automatically extracted using Gabor filters and wavelet transform and are locally matched using the Iterative Closest Point (ICP) algorithm. Our experiments show that Level 3 features carry significant discriminatory information. There is a relative reduction of 20 percent in the equal error rate (EER) of the matching system when Level 3 features are employed in combination with Level 1 and 2 features. This significant performance gain is consistently observed across various quality fingerprint images.

Algorithms↗

A modified SPIHT algorithm for image coding with a joint MSE and classification distortion measure.

The set partitioning in hierarchical trees (SPIHT) algorithm is an efficient wavelet-based progressive image-compression technique, designed to minimize the mean-squared error (MSE) between the original and decoded imagery. However, the MSE-based distortion measure is not in general well correlated with image-recognition quality, especially at low bit rates. Specifically, low-amplitude wavelet coefficients that may be important for classification are given low priority by conventional SPIHT. In this paper, we use the kernel matching pursuits (KMP) method to autonomously estimate the importance of each wavelet subband for distinguishing between different textures, with textural segmentation first performed via a hidden Markov tree. Based on subband importance determined via KMP, we scale the wavelet coefficients prior to SPIHT coding, with the goal of minimizing a Lagrangian distortion based jointly on the MSE and classification error. For comparison we consider Bayes tree-structured vector quantization (B-TSVQ), also designed to obtain a tradeoff between MSE and classification error. The performances of the original SPIHT, the modified SPIHT, and B-TSVQ are compared.

Algorithms↗