58,360 research outputs found

    Discriminant analysis of multivariate time series using wavelets

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    In analyzing ECG data, the main aim is to differentiate between the signal patterns of those of healthy subjects and those of individuals with specific heart conditions. We propose an approach for classifying multivariate ECG signals based on discriminant and wavelet analyzes. For this purpose we use multiple-scale wavelet variances and wavelet correlations to distinguish between the patterns of multivariate ECG signals based on the variability of the individual components of each ECG signal and the relationships between every pair of these components. Using the results of other ECG classification studies in the literature as references, we demonstrate that our approach applied to 12-lead ECG signals from a particular database, displays quite favourable performance. We also demonstrate with real and synthetic ECG data that our approach to classifying multivariate time series out performs other well-known approaches for classifying multivariate time series. In simulation studies using multivariate time series that have patterns that are different from that of the ECG signals, we also demonstrate very favourably performance of this approach when compared to these other approaches.Time series, Wavelet Variances, Wavelet Correlations, Discriminant Analysis

    POSSIBILITIES OF PERFORMING BANKRUPTCY DATA ANALYSIS USING TIME SERIES CLUSTERING

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    Prediction of corporate bankruptcy is a study topic of great interest. Under the conditions of the modern free market, early diagnostics of unfavourable development trends of company’s activity or bankruptcy becomes a matter of great importance. There is no general method which would allow one to forecast unfavourable consequence with a high confidence degree. This paper focuses on the analysis of the approaches that can be used to perform an early bankruptcy diagnostics- in previous research multivariate discriminant analysis (MDA), neural network based approach and rule extraction method have been examined. Lately, time series clustering approach has become popular and its feasibility for bankruptcy data analysis is being investigated. Experiments carried out validate the use of such methods in the given class of tasks. As a novelty, an attempt to apply time series clustering method to the analysis of bankruptcy data is made

    The search for biomarkers of facial eczema, following a sporidesmin challenge in dairy cows, using mass spectrometry and nuclear magnetic resonance of serum, urine, and milk : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Sciences at Massey University, Palmerston North, Manawatu, New Zealand

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    Facial eczema (FE) is a secondary photosensitisation disease of ruminants that is significant in terms of both its economic importance to New Zealand and its impact on animal welfare. The clinical photosensitivity signs, caused by the retention of phytoporphyrin, occur secondarily to hepatobiliary damage caused by the mycotoxin sporidesmin. Currently it is difficult to diagnose subclinical animals and those in the early stages of the disease. The project was aimed at applying new analytical and statistical techniques, to attempt the early diagnosis of FE in dairy cows following the administration of a single oral dose (0.24 mg/kg) of sporidesmin. Well-established traditional techniques including production parameters, liver enzyme (GGT, GDH) activity measurements, as well as measurements of phytoporphyrin by fluorescence spectroscopy were made for comparison. Serum, urine, and milk were analysed using 1H Nuclear Magnetic Resonance (NMR), multivariate analysis (MVA), and time series statistics. Urine and milk did not prove useful for identification of sporidesmin intoxication. Serum metabolites differed between treated cows before and after administration of the toxin, and could distinguish samples belonging to the clinical group. The metabolites that were identified as being relevant to this classification were a mixture of glycoproteins, carboxylic acids, ketone bodies, amino-acids, glutamate, and glycerol, which were elevated for treated cattle, and acetate, choline, isoleucine, trimethylamine N-oxide, lipids, lipoproteins, cholesterol, and -glucose, which showed decreased concentrations. Citrate was found to be at higher concentration in non-responders and subclinicals only. When serum was analysed using ultra performance liquid chromatography electrospray ionisation mass spectrometry (UPLC/ESI-MS) and UPLC tandem MS (MS/MS), only samples from clinical cows could be discriminated. The molecular ions involved could be tentatively identified as a combination of taurine- and glycine-conjugated bile acids. These bile acids all became elevated. This study confirmed that liver enzyme activities (GGT, GDH) and phytoporphyrin concentrations are not effective as markers of early stage sporidesmin damage. Additionally, the new techniques were unable to detect early stage FE. However, some markers of treated cows were identified. The research does provide a strong foundation for future applications of metabolomics analysis, with MVA and time series statistics, for early stage FE diagnosis

    Can we identify non-stationary dynamics of trial-to-trial variability?"

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    Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings
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