Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MITBIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data wits presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network. (c) 2005 Elsevier B.V. All rights reserved.

Modeling the effects of anesthesia on the electroencephalogram

Changes to the electroencephalogram (EEG) observed during general anesthesia are modeled with a physiological mean field theory of electrocortical activity. To this end a parametrization of the postsynaptic impulse response is introduced which takes into account pharmacological effects of anesthetic agents on neuronal ligand-gated ionic channels. Parameter sets for this improved theory are then identified which respect known anatomical constraints and predict mean firing rates and power spectra typically encountered in human subjects. Through parallelized simulations of the eight nonlinear, two-dimensional partial differential equations on a grid representing an entire human cortex, it is demonstrated that linear approximations are sufficient for the prediction of a range of quantitative EEG variables. More than 70 000 plausible parameter sets are finally selected and subjected to a simulated induction with the stereotypical inhaled general anesthetic isoflurane. Thereby 86 parameter sets are identified that exhibit a strong “biphasic” rise in total power, a feature often observed in experiments. A sensitivity study suggests that this “biphasic” behavior is distinguishable even at low agent concentrations. Finally, our results are briefly compared with previous work by other groups and an outlook on future fits to experimental data is provided.

On the application of optimal wavelet filter banks for ECG signal classification

This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

Automated artifact removal from the electroencephalogram; a comparative study

Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.

Albumin causes a synergistic increase in the antioxidant activity of green tea catechins in oil-in-water emulsions

Model oil-in-water emulsions containing epicatechin (EC) and epigallocatechin gallate (EGCG) showed a synergistic increase in stability in emulsions containing added albumin. EGCG showed a stronger synergy (35%) with ovalbumin than did EC. Oxidation of the oil was monitored by determining peroxide values and hexanal contents. The effect of bovine serum albumin (BSA) on model oil-in-water emulsions containing each of the green tea catechins [epicatechin gallate (ECG), EGCG, EC and epigallocatechin (EGC)] was studied during storage at 30 degrees C. The green tea catechins showed moderate antioxidant activity in the emulsions with the order of activity being ECG approximate to EGCG > EC > EGC. Although BSA had very little antioxidant activity in the absence of phenolic antioxidants, the combination of BSA with each of the catechins showed strong antioxidant activity. BSA, in combination with EC, EGCG or EGC, showing the strongest antioxidant activity with good stability after 45 days storage. Model experiments with the catechins stored with BSA in aqueous solutions confirmed that protein-catechin adducts with antioxidant activity were formed between the catechins and protein. The antioxidant activity of the separated protein-catechin adducts increased strongly with storage time and was stronger for EGCG and ECG than for EC or EGC. (c) 2006 Elsevier Ltd. All rights reserved.

Simulating global properties of electroencephalograms with minimal random neural networks

The human electroencephalogram (EEG) is globally characterized by a 1/f power spectrum superimposed with certain peaks, whereby the "alpha peak" in a frequency range of 8-14 Hz is the most prominent one for relaxed states of wakefulness. We present simulations of a minimal dynamical network model of leaky integrator neurons attached to the nodes of an evolving directed and weighted random graph (an Erdos-Renyi graph). We derive a model of the dendritic field potential (DFP) for the neurons leading to a simulated EEG that describes the global activity of the network. Depending on the network size, we find an oscillatory transition of the simulated EEG when the network reaches a critical connectivity. This transition, indicated by a suitably defined order parameter, is reflected by a sudden change of the network's topology when super-cycles are formed from merging isolated loops. After the oscillatory transition, the power spectra of simulated EEG time series exhibit a 1/f continuum superimposed with certain peaks. (c) 2007 Elsevier B.V. All rights reserved.

Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain-Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.

Single-trial event-related potential analysis for brain-computer interfaces

Abstract. Different types of mental activity are utilised as an input in Brain-Computer Interface (BCI) systems. One such activity type is based on Event-Related Potentials (ERPs). The characteristics of ERPs are not visible in single-trials, thus averaging over a number of trials is necessary before the signals become usable. An improvement in ERP-based BCI operation and system usability could be obtained if the use of single-trial ERP data was possible. The method of Independent Component Analysis (ICA) can be utilised to separate single-trial recordings of ERP data into components that correspond to ERP characteristics, background electroencephalogram (EEG) activity and other components with non- cerebral origin. Choice of specific components and their use to reconstruct “denoised” single-trial data could improve the signal quality, thus allowing the successful use of single-trial data without the need for averaging. This paper assesses single-trial ERP signals reconstructed using a selection of estimated components from the application of ICA on the raw ERP data. Signal improvement is measured using Contrast-To-Noise measures. It was found that such analysis improves the signal quality in all single-trials.

Rejoinder to 'Performance of different synchronisation measures in real data: A case study on electroencephalograhic signals

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga et al. [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga et al. in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga et al. in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.

Comment on "Performance of different synchronization measures in real data: A case study on electroencephalographic signals"

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.

Characterisation of soils containing naturally occurring radioactive material (NORM) using in situ and rapid laboratory techniques. Case study: South Terras, Cornwall

In situ analysis has become increasingly important for contaminated land investigation and remediation. At present, portable techniques are used mainly as scanning tools to assess the spread and magnitude of the contamination, and are an adjunct to conventional laboratory analyses. A site in Cornwall, containing naturally occurring radioactive material (NORM), provided an opportunity for Reading University PhD student Anna Kutner to compare analytical data collected in situ with data generated by laboratory-based methods. The preliminary results in this paper extend the author‟s poster presentation at last September‟s GeoSpec2010 conference held in Lancaster.

Reduction of invasive tests in chagasic patients with a modified self-organizing map

The applicability of AI methods to the Chagas' disease diagnosis is carried out by the use of Kohonen's self-organizing feature maps. Electrodiagnosis indicators calculated from ECG records are used as features in input vectors to train the network. Cross-validation results are used to modify the maps, providing an outstanding improvement to the interpretation of the resulting output. As a result, the map might be used to reduce the need for invasive explorations in chronic Chagas' disease.

A preliminary investigation of the impact of catechol-O-methyltransferase genotype on the absorption and metabolism of green tea catechins

Purpose Green tea is thought to possess many beneficial effects on human health. However, the extent of green tea polyphenol biotransformation may affect its proposed therapeutic effects. Catechol-O-methyltransferase (COMT), the enzyme responsible for polyphenolic methylation, has a common polymorphism in the genetic code at position 158 reported to result in a 40% reduction in enzyme activity in in vitro studies. The current preliminary study was designed to investigate the impact of COMT genotype on green tea catechin absorption and metabolism in humans. Methods Twenty participants (10 of each homozygous COMT genotype) were recruited, and plasma concentration profiles were produced for epigallocatechin gallate (EGCG), epigallocatechin (EGC), epicatechin gallate (ECG), epicatechin (EC) and 4′-O-methyl EGCG after 1.1 g of Sunphenon decaffeinated green tea extract (836 mg green tea catechins), with a meal given after 60 min. Results For the entire group, EGCG, EGC, EC, ECG and 4′-O-methyl EGCG reached maximum concentrations of 1.09, 0.41, 0.33, 0.16 and 0.08 μM at 81.5, 98.5, 99.0, 85.5 and 96.5 min, respectively. Bimodal curves were observed for the non-gallated green tea catechins EGC and EC as opposed to single-peaked curves for the gallated green tea catechins EGCG and ECG. No significant parametric differences between COMT genotype groups were found. Conclusions In conclusion, the COMT Val(158/108)Met does not appear to have a dramatic influence on EGCG absorption and elimination. However, further pharmacokinetic research is needed to substantiate these findings.

The effect of exogenous gonadotropins on ovarian function in goats actively immunized against inhibin

The aim of this investigation was to compare the ovarian response to superovulatory treatments in does before and after inhibin immunization, with a view to optimizing the superovulatory potential of the caprine ovary. To avoid interference by the ovarian cycle, the experiment was conducted out-of-season. At the onset of the experiment 48 does were subjected to treatment with an sc implant of the progestogen norgestomet, combined with a gonadotropin; eight does each received a single injection of 1200 IU eCG, 400 IU eCG or 2 mL physiological saline (control) or six injections (at 12 h intervals) constituting 16 or 5.4 AU pFSH. The does were mated and subjected to embryo collection 6 to 7 d later. Throughout the experiment ovarian function (by ultrasonography) and plasma levels of inhibin antibodies and progesterone were monitored. Of 40 does treated during the first part of the experiment, 48% showed estrus. The ovarian response in does treated with a high or low dose of eCG or a low dose of pFSH was barely in excess of the ovarian response in the saline-treated controls, whereas a superovulatory dose of pFSH (16 AU) gave a satisfactory response of, on average, 14.5 ovulations (yielding 8.8 flushed ova and embryos). Immediately after the does had been subjected to embryo collection they were actively immunized against inhibin by administering two injections of a recombinant α-subunit of ovine inhibin at four week intervals. All immunized does produced antibodies with the maximal titer reached two weeks after the second injection. Groups of immunized does were subjected to the same gonadotropin treatments as before (avoiding allocation of individuals to the same treatments). This time all does showed estrous symptoms. The ovulatory response to the various treatments, including the saline controls, was virtually identical, the overall average being 21.8 follicles and 9.1 ovulations. The average embryo yield per doe was 5.7. The results imply that inhibin acted as the key factor in determining the ovulatory response since no impact of any of the supplementary gonadotropins was noted in inhibin-immunized does. This finding gives rise to the notion that inhibin antibodies may act primarily by an intraovarian paracrine action rather than by reducing the suppressive action of inhibin on pituitary FSH release. Further, these findings confirm earlier reports that eCG is less suitable than FSH for inducing superovulation in goats, and indicate that active immunization against inhibin may be considered a viable alternative to using exogenous gonadotropin for inducing superovulation in goats.

Bridging a yawning chasm: EEG investigations into the debate concerning the role of the human mirror neuron system in contagious yawning

Ongoing debate in the literature concerns whether there is a link between contagious yawning and the human mirror neuron system (hMNS). One way of examining this issue is with the use of the electroencephalogram (EEG) to measure changes in mu activation during the observation of yawns. Mu oscillations are seen in the alpha bandwidth of the EEG (8–12 Hz) over sensorimotor areas. Previous work has shown that mu suppression is a useful index of hMNS activation and is sensitive to individual differences in empathy. In two experiments, we presented participants with videos of either people yawning or control stimuli. We found greater mu suppression for yawns than for controls over right motor and premotor areas, particularly for those scoring higher on traits of empathy. In a third experiment, auditory recordings of yawns were compared against electronically scrambled versions of the same yawns. We observed greater mu suppression for yawns than for the controls over right lateral premotor areas. Again, these findings were driven by those scoring highly on empathy. The results from these experiments support the notion that the hMNS is involved in contagious yawning, emphasise the link between contagious yawning and empathy, and stress the importance of good control stimuli.