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Biomedical subjects

A Bezerianos

Publications and source records attributed to A Bezerianos.

At least 19 recordsLinked to original sources
PubMed2005

New aspects of vulnerability in heterogeneous models of ventricular wall and its modulation by loss of cardiac sodium channel function.

This numerical study quantified the vulnerable period (VP) in heterogeneous models of the cardiac ventricular wall and its modulation by loss of cardiac sodium channel function (NaLOF). According to several articles, NaLOF prolongs the VP and therefore increases the risk of re-entrant arrhythmias, but the studies used uniform models, neglecting spatial variation of action potential duration (APD). Here, physiological transmural heterogeneity was introduced into one-dimensional cables of the Luo-Rudy model cells. Based on the results with paired S1-S2 stimulation, a generalised formula for the VP was proposed that takes into account APD dispersion, and new phenomena pertaining to the VP are described that are not present in homogeneous excitable media. Under normal conditions, the vulnerable period in the heterogeneous cable with M cells was in the range of 0-21 ms, depending on S2 localisation, but only 2.4 ms throughout the uniform fibre. Unidirectional propagation induced during the VP could be antegrade or retrograde, depending on the localisation of the test stimulus and cable parameters, but, in a uniform model, it was always in the retrograde direction. Reduced sodium channel conductance from control 16 mS microF(-1) to 4 mS microF(-1) decreased the maximum VP to 11 ms in the heterogeneous cable, but increased the VP to 3 ms in the homogeneous model. It was concluded that realistic models of cardiac vulnerability should take into account spatial variations of cellular refractoriness. Several new qualitative and quantitative aspects of the VP were revealed, and the modulation of the VP by NaLOF differed significantly in heterogeneous and homogeneous models.

Action Potentials↗
PubMed2005

Investigation of the effects of ischemic preconditioning on the HRV response to transient global ischemia using linear and nonlinear methods.

Ischemic preconditioning (IP) has been used as a strategy to prevent cell death in various organs, including the brain and the heart. Investigation of the effects of ischemic preconditioning mostly employed models with reduced complexity, such as cell cultures, tissue slices or perfused organ preparations. Although such models can provide valuable insight into the protective mechanism of preconditioning, the functional (re)organization of the control mechanisms at the level of the living organism cannot be assessed. The purpose of the present animal model study was to evaluate the effect of global ischemic preconditioning on the heart rate variability (HRV) response to the asphyxia insult. The data consisted of 4 h RR interval measurements recorded in five preconditioned and five non-preconditioned Wistar rats. Using linear (time and frequency domain) and nonlinear (approximate entropy and parameters of Poincare plots) measures, we evaluated the dynamic time course of the HRV response to the asphyxia insult and the effect of preconditioning on the autonomic neurocardiac control. Both the linear and nonlinear parameters indicate a faster recovery of the baseline HRV corresponding to the preconditioned groups, though only the spectral analysis identifies a statistically significant difference between the two groups. For the preconditioned group, at about 90 min after the asphyxic insult, the autonomic neural balance (measured by LF/HF ratio) appears fully recovered. The small variation of the rest of the parameters indicates the necessity of further investigation including the design of a larger study with a higher statistical power. Our results show for the first time that global ischemic preconditioning influences the HRV response to the asphyxia injury. The neuroprotective effect of preconditioning translates into a faster recovery of the basal HRV and the autonomic modulation of the heart.

Algorithms↗
PubMed2004

Characterization of heart rate variability changes following asphyxia in rats.

OBJECTIVES: A non-invasive method to monitor the functioning of the autonomous nervous system consists in heart rate variability (HRV) analysis. The aim of this study was to investigate the changes on HRV after an asphyxia experiment in rats, using several linear (time and frequency domain) and nonlinear parameters (approximate entropy, SD1 and SD2 indices derived from Poincare plots). METHODS: The experiments involved the study of HRV changes after cardiac arrest (CA) resulting from 5 min of hypoxia and asphyxia, followed by manual resuscitation and return of spontaneous circulation. 5 min stationary periods of RR intervals were selected for further analysis from 5 rats in following distinct situations: 1) baseline, 2) 30 min after CA, 3) 60 min after CA, 4) 90 min after CA, 5) 120 min after CA, 6) 150 min after CA. The ANS contribution has been delineated based on time and frequency domain analysis. RESULTS AND CONCLUSIONS: The results indicate that the recovery process following the asphyxia cardiac arrest reflects the impaired functioning of the autonomic nervous system. Both linear and nonlinear parameters track the different phases of the experiment, with an increased sensitivity displayed by the approximate entropy (ApEn). After 150 min the ApEn RRI parameter recovers to its baseline value. The results forward the ApEn as a more sensitive parameter of the recovery process following the asphyxia.

Animals↗
PubMed2003

Time-dependent entropy estimation of EEG rhythm changes following brain ischemia.

Our approach is motivated by the need to generate a rigorous measure of the degree of disorder (or complexity) of the EEG signal in brain injury. Entropy is a method to quantify the order/disorder of a time series. It is the first time that a time-dependent entropy (TDE) is used in the quantification of brain injury level. The TDE was sensitive enough to monitor the significant changes in the subject's brain rhythms during recovery from global ischemic brain injury. Among the different entropy measures, we used Tsallis entropy. This entropy is parametrized and is able to match with the particular properties of EEG, like long-range rhythms, spikes, and bursts. The method was tested in a signal composed of segments of synthetic signals (Gaussian and uniform distributions) and segments of real signals. The real signal segments were extracted from normal EEG, EEG recordings from early recovery, and normal EEG corrupted by simulated spikes and bursts. Adult Wistar rats were subjected to asphyxia-cardiac arrest for 3 and 5 min. The TDE detected the pattern of ischemia-induced EEG alterations and was able to discriminate the different injury levels. Two parameters seem to be good descriptors of the recovery process; the mean entropy and the variance of the estimate followed opposite trends, with the mean entropy decreasing and its variance increasing with injury.

Algorithms↗
PubMed2001

Removal of ECG interference from the EEG recordings in small animals using independent component analysis.

In experiments involving small animals, the electroencephalogram (EEG) recorded during severe injury and accompanying resuscitation exhibit the strong presence of electrocardiogram (ECG). For improved quantitative EEG (qEEG) analysis, it is therefore imperative to remove ECG interference from EEG. In this paper, we validate the use of independent component analysis (ICA) to effectively suppress the interference of ECG from EEG recordings during normal activity, asphyxia and recovery following asphyxia. Two channels of EEG from five rats were recorded continuously for 2 h. Simultaneous recording of one channel ECG was also made. Epochs of 4 s and 1 min were selected from baseline, asphyxia and recovery (every 10 min) and their independent components and power spectra were calculated. The improvement in normalized power spectrum of EEG obtained for all animals was 7.71+/-3.63 db at the 3rd minute of recovery and dropped to 1.15+/-0.60 db at 63rd minute. The application of ICA has been particularly useful when the power of EEG is low, such as that observed during early brain hypoxic-asphyxic injury. The method is also useful in situations where accurate indications of EEG signal power and frequency content are needed.

Algorithms↗
PubMed2001

A theoretical analysis of acute ischemia and infarction using ECG reconstruction on a 2-D model of myocardium.

We developed a two-dimensional ventricular tissue model in order to probe the determinants of electrocardiographic (ECG) morphology during acute and chronic ischemia. Hyperkalemia was simulated by step changes in [K+]out, while acidosis was induced by reducing Na+ and Ca2+ conductances. Hypoxia was introduced by its effect on potassium activity. During the initial moments of ischemia, ECG changes were characterized by increases in QRS amplitude and ST segment shortening, followed in the advanced phase by ST baseline elevation, T conformation changes, widening of the QRS and significant decreases in QRS amplitude in spite of an enlarged Q. During each phase, potential proarrhythmic mechanisms were investigated. The presence of unexcitable regions of simulated myocardial infarction led to polymorphic ECG. We also observed a nonuniform deflection of the ST segment from beat to beat. We used similar protocols to explore the responses of infarcted myocardium after impairment resolving. We found that despite irreversible uncoupling of the necrotic region, the restored normal ionic concentrations produced an isopotential ST segment and monomorphic ECG complexes, while an enlarged Q wave was still visible. In summary, these numerical experiments indicate the possibility to track in the ECG pathologic changes following the altered electrophysiology of the ischemic heart.

Action Potentials↗
PubMed2001

Novel Bayesian multiscale method for speckle removal in medical ultrasound images.

A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, we design a Bayesian estimator that exploits these statistics. We use the alpha-stable model to develop a blind noise-removal processor that performs a nonlinear operation on the data. Finally, we compare our technique with current state-of-the-art soft and hard thresholding methods applied on actual ultrasound medical images and we quantify the achieved performance improvement.

Bayes Theorem↗
PubMed2000

Hierarchical state space partitioning with a network self-organising map for the recognition of ST-T segment changes.

The problem of maximising the performance of ST-T segment automatic recognition for ischaemia detection is a difficult pattern classification problem. The paper proposes the network self-organising map (NetSOM) model as an enhancement to the Kohonen self-organised map (SOM) model. This model is capable of effectively decomposing complex large-scale pattern classification problems into a number of partitions, each of which is more manageable with a local classification device. The NetSOM attempts to generalize the regularization and ordering potential of the basic SOM from the space of vectors to the space of approximating functions. It becomes a device for the ordering of local experts (i.e. independent neural networks) over its lattice of neurons and for their selection and co-ordination. Each local expert is an independent neural network that is trained and activated under the control of the NetSOM. This method is evaluated with examples from the European ST-T database. The first results obtained after the application of NetSOM to ST-T segment change recognition show a significant improvement in the performance compared with that obtained with monolithic approaches, i.e. with single network types. The basic SOM model has attained an average ischaemic beat sensitivity of 73.6% and an average ischaemic beat predictivity of 68.3%. The work reports and discusses the improvements that have been obtained from the implementation of a NetSOM classification system with both multilayer perceptrons and radial basis function (RBF) networks as local experts for the ST-T segment change problem. Specifically, the NetSOM with multilayer perceptrons (radial basis functions) as local experts has improved the results over the basic SOM to an average ischaemic beat sensitivity of 75.9% (77.7%) and an average ischaemic beat predictivity of 72.5% (74.1%).

Electrocardiography↗
PubMed1999

Radial basis function neural networks for the characterization of heart rate variability dynamics.

This study introduces new neural network based methods for the assessment of the dynamics of the heart rate variability (HRV) signal. The heart rate regulation is assessed as a dynamical system operating in chaotic regimes. Radial-basis function (RBF) networks are applied as a tool for learning and predicting the HRV dynamics. HRV signals are analyzed from normal subjects before and after pharmacological autonomic nervous system (ANS) blockade and from diabetic patients with dysfunctional ANS. The heart rate of normal subjects presents notable predictability. The prediction error is minimized, in fewer degrees of freedom, in the case of diabetic patients. However, for the case of pharmacological ANS blockade, although correlation dimension approaches indicate significant reduction in complexity, the RBF networks fail to reconstruct adequately the underlying dynamics. The transient attributes of the HRV dynamics under the pharmacological disturbance is elucidated as the explanation for the prediction inability.

Atropine↗
PubMed1999

Nonlinear analysis of the performance and reliability of wavelet singularity detection based denoising for Doppler ultrasound fetal heart rate signals.

Many studies on the physiology of the cardiovascular system revealed that nonlinear chaotic dynamics govern the generation of the heart rate signal. This is also valid for the fetal heart rate (FHR) variability, where however the variability is affected by many more factors and is significantly more complicated than for the adult case. Recently an adaptive wavelet denoising method for the Doppler ultrasound FHR recordings has been introduced. In this paper the performance and reliability of that method is confirmed by the observation that for the wavelet denoised FHR signal, a deterministic nonlinear structure, which was concealed by the noise, becomes apparent. It provides strong evidence that the denoising process removes actual noise components and can therefore be utilized for the improvement of the signal quality. Hence by observing after denoising a significant improvement of the 'chaoticity' of the FHR signal we obtain strong evidence for the reliability and efficiency of the wavelet based denoising method. The estimation of the chaoticity of the FHR signal before and after the denoising is approached with three nonlinear analysis methods. First, the rescaled scale analysis (RSA) technique reveals that the denoising process increases the Hurst exponent parameter as happens when additive noise is removed from a chaotic signal. Second, the nonlinear prediction error evaluated with radial basis function (RBF) prediction networks is significantly lower at the denoised signal. The significant gain in predictability can be attributed to the drastic reduction of the additive noise from the signal by the denoising algorithm. Moreover, the evaluation of the correlation coefficient between actual and neural network predicted values as a function of the prediction time displays characteristics of chaos only for the denoised signal. Third, a chaotic attractor, reconstructed with the embedding dimension technique, becomes evident for the denoised signal, while it is completely obscured for the original signals. The correlation dimension of the reconstructed attractor for the denoised signal tends to reach a value independent of the embedding dimension, a sign of deterministic chaotic signal. In contrast for the original signal the correlation dimension increases steadily with the embedding dimension, a fact that indicates strong contribution of noise.

Adult↗
PubMed1998

Comparison of computer simulated and phantom measured phase variance in the study of trabecular bone.

A new method using magnetic resonance phase images for the assessment of trabecular bone structure has recently been proposed. To test this method, a mathematical model is developed which calculates the phase distribution in gradient echo acquired phase images of a structure of Pyrex glass rods immersed in a copper sulfate solution. Several experiments were performed using a phantom built in the same way as the structure used in the mathematical model. The results from the model are compared with those from the phantom tests, and the influence of resolution and bone area fraction on the phase dispersion is studied. The good correlation between theoretical and experimental results shows that phase variance increases with increasing resolution and bone density. However, the dependence of variance on bone density is less prominent for large pixel sizes.

Bone Density↗
PubMed1998

Beat-to-beat wavelet variance of the QRS complex as a marker of arrhythmogenic substrate in ventricular tachycardia patients.

This study proposes a wavelet transform based technique to assess the beat-to-beat variation of the QRS signal in post-myocardial infarction patients with sustained monomorphic ventricular tachycardia. Recent electrophysiological investigations suggested that the diminished synchrony between the normal myocardium and the scarred arrhythmogenic tissue bordering a myocardial infarction area gives rise to beat-variable ECG signal components. Using a mathematical model of small variations in a largely repetitive waveform, we show that the inherent alignment errors (trigger jitter) of the high-resolution ECG (HRECG) can artificially increase the value of the time-domain beat-to-beat variance, making it less valuable as a marker of beat-variable signal components. To overcome this drawback, we propose the wavelet based approach which discriminates between the different factors responsible for the beat variability (the alignment error and the beat-variable signal components). The Morlet wavelet transform is performed on HRECG signals from normal individuals (control group) and postmyocardial infarction patients with documented ventricular tachycardia. Electrical variability is quantitatively assessed via the beat-to-beat wavelet variance measurements. A marker of arrhythmogenic induced variance which achieves a good performance in discrimination of ventricular tachycardia patients from normal subjects was found between 200 Hz and 300 Hz. This finding is in agreement with the proposed mathematical model which states that the useful part of the time-frequency map is shifted upward in a precise mathematical way, as the variance induced by the beat-variable arrhythmogenic signals depend on the frequency characteristics of the first derivative of these signals. We conclude that the dynamics of the arrhythmogenic substrate as revealed by the beat-to-beat wavelet variance can be a new estimator of ventricular tachycardia risk.

Algorithms↗
PubMed1997

Robust moving averages, with Hopfield neural network implementation, for monitoring evoked potential signals.

This technical note describes a robust version of moving averages, that enables reliable monitoring of the evoked potential (EP) signals. A cluster analysis (CA) procedure is introduced to robustify the signal averaging (SA). It is implemented via a Hopfield neural network (HNN), which performs selection of the trials forming a cluster around the current state of the EP signal. The core of this cluster serves as an estimate of the instantaneous EP. The effectiveness of the method, indicated by application to real data, and its computation efficiency, due to the use of simple matrix operations, makes it very promising for clinical observations.

Electroencephalography↗
PubMed1997

Denoising of the fetal heart rate signal with non-linear filtering of the wavelet transform maxima.

The fetal heart rate (FHR) signal provides valuable information for fetal development and well-being. However, the FHR traces derived from present-day ultrasound cardiotocographs are not of the desired quality. The paper applies the wavelet transform (WT) in order to denoise effectively the FHR signal. The denoising procedure analyses the evolution of the WT maxima across scales. The singularities of the signal create wavelet maxima with different properties from those of the induced noise. Since it is difficult to formulate precise rules that distinguish between the wavelet maxima of the FHR signal from those of the noise we have trained a neural network for this classification task. The neural network draws out successfully the noise induced wavelet maxima. An improved FHR signal can be obtained from the coarser wavelet approximation signal component and the filtered wavelet maxima by means of the inverse dyadic wavelet transform. Also, feature extraction and processing algorithms can be defined on the denoised wavelet coefficients (instead of on the original signal).

Artifacts↗
PubMed1997

Use of wavelet analysis for contrast enhancement in mammography.

A wavelet analysis approach to contrast enhancement in mammography is presented. The approach consists of application of non-linear enhancement of multiscale gradient magnitudes utilising edge location information. The performance of wavelet analysis is evaluated by means of a breast phantom. Preliminary results indicate the value of the approach in contrast enhancement and edge preservation for both simulated tumours and microcalcifications.

Breast Neoplasms↗
PubMed1997

An intelligent system for the tracking--localization of changes & exploratory analysis--of long-term ECG.

An interactive methodology for the analysis of long-term ECG is introduced. It is an anthropomimetic technique and consists of three parts. At the first stage, the clinicians' scan of the ECG traces is imitated and changes in the shape of QRS are quantified. In the sequel, a clinician involves in the interpretation of the most prominent changes providing the patient-dependent prototypes for the subsequent machine learning procedure. Finally, a classification scheme incorporates the portion of medical knowledge needed to explore the whole patient's ECG. This scheme, being very robust to noise, presents excellent generalization properties and can serve as a reliable automation in a future examination of the certain subject.

Arrhythmias, Cardiac↗