Empirical mode decomposition: a novel technique for the study of tremor time series

Tremor is a clinical feature characterized by oscillations of a part of the body. The detection and study of tremor is an important step in investigations seeking to explain underlying control strategies of the central nervous system under natural (or physiological) and pathological conditions. It is well established that tremorous activity is composed of deterministic and stochastic components. For this reason, the use of digital signal processing techniques (DSP) which take into account the nonlinearity and nonstationarity of such signals may bring new information into the signal analysis which is often obscured by traditional linear techniques (e.g. Fourier analysis). In this context, this paper introduces the application of the empirical mode decomposition (EMD) and Hilbert spectrum (HS), which are relatively new DSP techniques for the analysis of nonlinear and nonstationary time-series, for the study of tremor. Our results, obtained from the analysis of experimental signals collected from 31 patients with different neurological conditions, showed that the EMD could automatically decompose acquired signals into basic components, called intrinsic mode functions (IMFs), representing tremorous and voluntary activity. The identification of a physical meaning for IMFs in the context of tremor analysis suggests an alternative and new way of detecting tremorous activity. These results may be relevant for those applications requiring automatic detection of tremor. Furthermore, the energy of IMFs was visualized as a function of time and frequency by means of the HS. This analysis showed that the variation of energy of tremorous and voluntary activity could be distinguished and characterized on the HS. Such results may be relevant for those applications aiming to identify neurological disorders. In general, both the HS and EMD demonstrated to be very useful to perform objective analysis of any kind of tremor and can therefore be potentially used to perform functional assessment.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition

Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony.

Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis

Transient episodes of synchronisation of neuronal activity in particular frequency ranges are thought to underlie cognition. Empirical mode decomposition phase locking (EMDPL) analysis is a method for determining the frequency and timing of phase synchrony that is adaptive to intrinsic oscillations within data, alleviating the need for arbitrary bandpass filter cut-off selection. It is extended here to address the choice of reference electrode and removal of spurious synchrony resulting from volume conduction. Spline Laplacian transformation and independent component analysis (ICA) are performed as pre-processing steps, and preservation of phase synchrony between synthetic signals. combined using a simple forward model, is demonstrated. The method is contrasted with use of bandpass filtering following the same preprocessing steps, and filter cut-offs are shown to influence synchrony detection markedly. Furthermore, an approach to the assessment of multiple EEG trials using the method is introduced, and the assessment of statistical significance of phase locking episodes is extended to render it adaptive to local phase synchrony levels. EMDPL is validated in the analysis of real EEG data, during finger tapping. The time course of event-related (de)synchronisation (ERD/ERS) is shown to differ from that of longer range phase locking episodes, implying different roles for these different types of synchronisation. It is suggested that the increase in phase locking which occurs just prior to movement, coinciding with a reduction in power (or ERD) may result from selection of the neural assembly relevant to the particular movement. (C) 2009 Elsevier B.V. All rights reserved.

Altered expression of cell cycle proteins and prolonged duration of cardiac myocyte hyperplasia in p27KIP1 knockout mice

The precise role of cell cycle-dependent molecules in controlling the switch from cardiac myocyte hyperplasia to hypertrophy remains to be determined. We report that loss of p27(KIP1) in the mouse results in a significant increase in heart size and in the total number of cardiac myocytes. In comparison to p27(KIP1)+/+ myocytes, the percentage of neonatal p27(KIP1)-/- myocytes in S phase was increased significantly, concomitant with a significant decrease in the percentage of G(0)/G(1) cells. The expressions of proliferating cell nuclear antigen, G(1)/S and G(2)/M phase-acting cyclins, and cyclin-dependent kinases (CDKs) were upregulated significantly in ventricular tissue obtained from early neonatal p27(KIP1)-/- mice, concomitant with a substantial decrease in the expressions of G(1) phase-acting cyclins and CDKs. Furthermore, mRNA expressions of the embryonic genes atrial natriuretic factor and alpha-skeletal actin were detectable at significant levels in neonatal and adult p27(KIP1)-/- mouse hearts but were undetectable in p27(KIP1)+/+ hearts. In addition, loss of p27(KIP1) was not compensated for by the upregulation of other CDK inhibitors. Thus, the loss of p27(KIP1) results in prolonged proliferation of the mouse cardiac myocyte and perturbation of myocyte hypertrophy.

Expressions and activities of cell cycle regulatory molecules during the transition from myocyte hyperplasia to hypertrophy

The role of cell cycle dependent molecules in controlling the switch from cardiac myocyte hyperplasia to hypertrophy remains unclear, although in the rat this process occurs between day 3 and 4 after birth. In this study we have determined (1) cell cycle profiles by fluorescence activated cell sorting (FACS); and (2) expressions, co-expressions and activities of a number of cyclins, cyclin-dependent kinases (CDKs) and CDK inhibitors by reverse transcriptase-polymerase chain reaction (RT-PCR), immunoblotting andin vitrokinase assays in freshly isolated rat cardiac myocytes obtained from 2, 3, 4 and 5-day-old animals. The percentage of myocytes found in the S phase of the cell cycle decreased significantly during the transition from hyperplasia to hypertrophy (5.5, 3.5, 2.3 and 1.9% of cells in 2-, 3-, 4- and 5-day-old myocytes, respectively,P<0.05), concomitant with a significant increase in the percentage of G0/G1phase cells. At the molecular level, the expressions and activities of G1/S and G2/M phase acting cyclins and CDKs were downregulated significantly during the transition from hyperplasia to hypertrophy, whereas the expressions and activities of G1phase acting cyclins and CDKs were upregulated significantly during this transition. In addition, p21CIP1- and p27KIP1- associated CDK kinase activities remained relatively constant when histone H1 was used as a substrate, whereas phosphorylation of the retinoblastoma protein was upregulated significantly during the transition from hyperplasia to hypertrophy. Thus, there is a progressive and significant G0/G1phase blockade during the transition from myocyte hyperplasia to hypertrophy. Whilst CDK2 and cdc2 may be pivotal in the withdrawal of cardiac myocytes from the cell cycle, CDK4 and CDK6 may be critical for maintaining hypertrophic growth of the myocyte during development.

Cell cycle profiles and expression of p21CIP1 and P27KIP1 during myocyte development

The ability of the cardiac myocyte to divide ceases shortly after birth. Thus, following severe injury, e.g., during myocardial infarction, the mature heart is unable to regenerate new tissue to replace the dead or damaged tissue. The identification of the molecules controlling the cessation of myocyte cell division may lead to therapeutic strategies which aim to re-populate the damaged myocardial area. Hence, we have determined the cell cycle profile, expressions and activities of the cyclin-dependent kinase inhibitors (CDKIs), p21CIP1 and p27KIP1, during rat ventricular myocyte development. Fluorescent activated cell sorting (FACS) analyses showed the percentage of S phase myocytes to be decreased significantly throughout development, concomitant with a significant increase in the percentage of G0/G1 and G2/M phase cells. The expression of p21CIP1 and p27KIP1 increased significantly throughout cardiac development and complexed differentially with a number of cyclins and CDKs. Furthermore, an adult myocyte extract reduced neonatal myocyte CDK2 kinase activity significantly (>30%, p<0.05) whereas immunodepletion of p21CIP1 from adult lysates restored CDK2 kinase activity. Thus, p21CIP1 and p27KIP1 may be important for the withdrawal of cardiac myocytes from the cell cycle and for maintaining the G0/G1 and G2/M phase blockades.

The cell cycle and drug discovery: The promise and the hope

In recent years, there have been major developments in the understanding of the cell cycle. It is now known that normal cellular proliferation is tightly regulated by the activation and deactivation of a series of proteins that constitute the cell cycle machinery. The expression and activity of components of the cell cycle can be altered during the development of a variety of diseases where aberrant proliferation contributes to the pathology of the illness. Apart from yielding a new source of untapped therapeutic targets, it is likely that manipulating the activity of such proteins in diseased states will provide an important route for treating proliferative disorders, and the opportunity to develop a novel class of future medicines.

Arresting developments in the cardiac myocyte cell cycle: Role of cyclin-dependent kinase inhibitors

Like most other cells in the body, foetal and neonatal cardiac myocytes are able to divide and proliferate. However, the ability of these cells to undergo cell division decreases progressively during development such that adult myocytes are unable to divide. A major problem arising from this inability of adult cardiac myocytes to proliferate is that the mature heart is unable to regenerate new myocardial tissue following severe injury, e.g. infarction, which can lead to compromised cardiac pump function and even death. Studies in proliferating cells have identified a group of genes and proteins that controls cell division. These proteins include cyclins, cyclin-dependent kinases (CDKs) and CDK inhibitors (CDKIs), which interact with each other to form complexes that are essential for controlling normal cell cycle progression. A variety of other proteins, e.g. the retinoblastoma protein (pRb) and members of the E2F family of transcription factors, also can interact with, and modulate the activities of, these complexes. Despite the major role that these proteins play in other cell types, little was known until recently about their existence and activities in immature (proliferating) or mature (non-proliferating) cardiac myocytes. The reason(s) why cardiac myocytes lose their ability to divide during development remains unknown, but if strategies were developed to understand the mechanisms underlying cardiac myocyte growth, it could open up new avenues for the treatment of cardiovascular disease. In this article, we shall review the function of the cell cycle machinery and outline some of our recent findings pertaining to the involvement of the cell cycle in modulating cardiac myocyte growth and hypertrophy.

Thermal decomposition of potassium hexanitronickelate(II) hydrate

The thermal decomposition of the complex K-4[Ni(NO2)6]center dot H2O has been investigated over the temperature range 25-600 degrees C by a combination of infrared spectroscopy, powder X-ray diffraction, FAB-mass spectrometry and elemental analysis. The first stage of reaction is loss of water and isomerisation of one of the coordinated nitro groups to form the complex K-4 [Ni(NO2)(4) (ONO)]center dot NO2. At temperatures around 200 degrees C the remaining nitro groups within the complex isomerise to the chelating nitrite form and this process acts as a precursor to the loss of NO2 gas at temperatures above 270 degrees C. The product, which is stable up to 600 degrees C, is the complex K-4[Ni(ONO)(4)]center dot NO2, where the nickel atom is formally in the +1 oxidation state. (c) 2005 Elsevier B.V. All rights reserved.