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Fast Electrocardiogram Amplifier Recovery after Defibrillation Shock  [PDF]
Ivan Dotsinsky,Tatyana Neycheva
Bioautomation , 2005,
Abstract: A procedure for fast ECG amplifier recovery after defibrillation shocks was developed and simulated in the MATLAB environment. Exponentially decaying post-shock voltages have been recorded. Signals from the AHA database are taken and mixed with the recorded exponential disturbances. The algorithm applies moving averaging (comb filter) on the compound input signal, thereby obtaining the samples of the disturbance. They are currently subtracted from the input signal. The results obtained show that its recovery is practically instantaneous.
FTSPlot: Fast Time Series Visualization for Large Datasets  [PDF]
Michael Riss
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0094694
Abstract: The analysis of electrophysiological recordings often involves visual inspection of time series data to locate specific experiment epochs, mask artifacts, and verify the results of signal processing steps, such as filtering or spike detection. Long-term experiments with continuous data acquisition generate large amounts of data. Rapid browsing through these massive datasets poses a challenge to conventional data plotting software because the plotting time increases proportionately to the increase in the volume of data. This paper presents FTSPlot, which is a visualization concept for large-scale time series datasets using techniques from the field of high performance computer graphics, such as hierarchic level of detail and out-of-core data handling. In a preprocessing step, time series data, event, and interval annotations are converted into an optimized data format, which then permits fast, interactive visualization. The preprocessing step has a computational complexity of ; the visualization itself can be done with a complexity of and is therefore independent of the amount of data. A demonstration prototype has been implemented and benchmarks show that the technology is capable of displaying large amounts of time series data, event, and interval annotations lag-free with ms. The current 64-bit implementation theoretically supports datasets with up to bytes, on the x86_64 architecture currently up to bytes are supported, and benchmarks have been conducted with bytes/1 TiB or double precision samples. The presented software is freely available and can be included as a Qt GUI component in future software projects, providing a standard visualization method for long-term electrophysiological experiments.
Power Series Composition and Change of Basis  [PDF]
Alin Bostan,Bruno Salvy,éric Schost
Computer Science , 2008, DOI: 10.1145/1390768.1390806
Abstract: Efficient algorithms are known for many operations on truncated power series (multiplication, powering, exponential, ...). Composition is a more complex task. We isolate a large class of power series for which composition can be performed efficiently. We deduce fast algorithms for converting polynomials between various bases, including Euler, Bernoulli, Fibonacci, and the orthogonal Laguerre, Hermite, Jacobi, Krawtchouk, Meixner and Meixner-Pollaczek.
Ultra-Fast Shapelets for Time Series Classification  [PDF]
Martin Wistuba,Josif Grabocka,Lars Schmidt-Thieme
Computer Science , 2015,
Abstract: Time series shapelets are discriminative subsequences and their similarity to a time series can be used for time series classification. Since the discovery of time series shapelets is costly in terms of time, the applicability on long or multivariate time series is difficult. In this work we propose Ultra-Fast Shapelets that uses a number of random shapelets. It is shown that Ultra-Fast Shapelets yield the same prediction quality as current state-of-the-art shapelet-based time series classifiers that carefully select the shapelets by being by up to three orders of magnitudes. Since this method allows a ultra-fast shapelet discovery, using shapelets for long multivariate time series classification becomes feasible. A method for using shapelets for multivariate time series is proposed and Ultra-Fast Shapelets is proven to be successful in comparison to state-of-the-art multivariate time series classifiers on 15 multivariate time series datasets from various domains. Finally, time series derivatives that have proven to be useful for other time series classifiers are investigated for the shapelet-based classifiers. It is shown that they have a positive impact and that they are easy to integrate with a simple preprocessing step, without the need of adapting the shapelet discovery algorithm.
A comparative analysis of preprocessing techniques of cardiac event series for the study of heart rhythm variability using simulated signals
Guimar?es, H.N.;Santos, R.A.S.;
Brazilian Journal of Medical and Biological Research , 1998, DOI: 10.1590/S0100-879X1998000300015
Abstract: in the present study, using noise-free simulated signals, we performed a comparative examination of several preprocessing techniques that are used to transform the cardiac event series in a regularly sampled time series, appropriate for spectral analysis of heart rhythm variability (hrv). first, a group of noise-free simulated point event series, which represents a time series of heartbeats, was generated by an integral pulse frequency modulation model. in order to evaluate the performance of the preprocessing methods, the differences between the spectra of the preprocessed simulated signals and the true spectrum (spectrum of the model input modulating signals) were surveyed by visual analysis and by contrasting merit indices. it is desired that estimated spectra match the true spectrum as close as possible, showing a minimum of harmonic components and other artifacts. the merit indices proposed to quantify these mismatches were the leakage rate, defined as a measure of leakage components (located outside some narrow windows centered at frequencies of model input modulating signals) with respect to the whole spectral components, and the numbers of leakage components with amplitudes greater than 1%, 5% and 10% of the total spectral components. our data, obtained from a noise-free simulation, indicate that the utilization of heart rate values instead of heart period values in the derivation of signals representative of heart rhythm results in more accurate spectra. furthermore, our data support the efficiency of the widely used preprocessing technique based on the convolution of inverse interval function values with a rectangular window, and suggest the preprocessing technique based on a cubic polynomial interpolation of inverse interval function values and succeeding spectral analysis as another efficient and fast method for the analysis of hrv signals
A comparative analysis of preprocessing techniques of cardiac event series for the study of heart rhythm variability using simulated signals  [cached]
Guimar?es H.N.,Santos R.A.S.
Brazilian Journal of Medical and Biological Research , 1998,
Abstract: In the present study, using noise-free simulated signals, we performed a comparative examination of several preprocessing techniques that are used to transform the cardiac event series in a regularly sampled time series, appropriate for spectral analysis of heart rhythm variability (HRV). First, a group of noise-free simulated point event series, which represents a time series of heartbeats, was generated by an integral pulse frequency modulation model. In order to evaluate the performance of the preprocessing methods, the differences between the spectra of the preprocessed simulated signals and the true spectrum (spectrum of the model input modulating signals) were surveyed by visual analysis and by contrasting merit indices. It is desired that estimated spectra match the true spectrum as close as possible, showing a minimum of harmonic components and other artifacts. The merit indices proposed to quantify these mismatches were the leakage rate, defined as a measure of leakage components (located outside some narrow windows centered at frequencies of model input modulating signals) with respect to the whole spectral components, and the numbers of leakage components with amplitudes greater than 1%, 5% and 10% of the total spectral components. Our data, obtained from a noise-free simulation, indicate that the utilization of heart rate values instead of heart period values in the derivation of signals representative of heart rhythm results in more accurate spectra. Furthermore, our data support the efficiency of the widely used preprocessing technique based on the convolution of inverse interval function values with a rectangular window, and suggest the preprocessing technique based on a cubic polynomial interpolation of inverse interval function values and succeeding spectral analysis as another efficient and fast method for the analysis of HRV signals
Fast Converging Series for Riemann Zeta Function  [PDF]
Hannu Olkkonen, Juuso T. Olkkonen
Open Journal of Discrete Mathematics (OJDM) , 2012, DOI: 10.4236/ojdm.2012.24025
Abstract: Riemann zeta function has a key role in number theory and in its applications. In this paper we present a new fast converging series for . Applications of the series include the computation of the and recursive computation of , and generally . We discuss on the production of irrational number sequences e.g. for encryption coding and zeta function maps for analysis and synthesis of log-time sampled signals.
Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series
Joon Lee, Shamim Nemati, Ikaro Silva, Bradley A Edwards, James P Butler, Atul Malhotra
BioMedical Engineering OnLine , 2012, DOI: 10.1186/1475-925x-11-19
Abstract: With respect to detection of increased coupling strength, we compared three transfer entropy estimation techniques using both simulated time series and respiratory recordings from lambs. The following estimation methods were analyzed: fixed-binning with ranking, kernel density estimation (KDE), and the Darbellay-Vajda (D-V) adaptive partitioning algorithm extended to three dimensions. In the simulated experiment, sample size was varied from 50 to 200, while coupling strength was increased. In order to introduce outliers, the heavy-tailed Laplace distribution was utilized. In the lamb experiment, the objective was to detect increased respiratory-related chemosensitivity to O2 and CO2 induced by a drug, domperidone. Specifically, the separate influence of end-tidal PO2 and PCO2 on minute ventilation ( V ˙ E ) before and after administration of domperidone was analyzed.In the simulation, KDE detected increased coupling strength at the lowest SNR among the three methods. In the lamb experiment, D-V partitioning resulted in the statistically strongest increase in transfer entropy post-domperidone for P O 2 → V ˙ E . In addition, D-V partitioning was the only method that could detect an increase in transfer entropy for P C O 2 → V ˙ E , in agreement with experimental findings.Transfer entropy is capable of detecting directional coupling changes in non-linear biomedical time series analysis featuring a small number of observations and presence of outliers. The results of this study suggest that fixed-binning, even with ranking, is too primitive, and although there is no clear winner between KDE and D-V partitioning, the reader should note that KDE requires more computational time and extensive parameter selection than D-V partitioning. We hope this study provides a guideline for selection of an appropriate transfer entropy estimation method.In multi-variable time series analysis, a common subject of interest is the coupling among the varia
Person Identification System Based on Electrocardiogram Signal Using LabVIEW  [PDF]
Noureddine BELGACEM,Fethi BEREKSI-REGUIG,Amine NAIT-ALI,Régis FOURNIER
International Journal on Computer Science and Engineering , 2012,
Abstract: This paper presents a method to analyze electrocardiogram (ECG) signal, extract the features, for the real time human identification. Data were obtained from short-term Lead-I ECG records (onlyone lead) of forty students at Paris Est University (UPEC). Signal averaging was applied to generate ECG databases and templates for reducing the noise recorded with palm ECG signals. Time domain signalprocessing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization. In this paper, an ECG biometric recognition method, that needs detection of one fiducial point only is introduced, based on classification of coefficients from the Fast Fourier Transform (FFT) of the Autocorrelation (AC) sequence of ECG data segments. The FFT is used to reduce extracted features from ECG signals. A 100% subject recognition rate for healthy subjects can be achieved for parameters that are suitable for the database.
Wavelet analysis on paleomagnetic (and computer simulated) VGP time series  [cached]
S. Lorito,G. Giberti,A. Siniscalchi,M. Iorio
Annals of Geophysics , 2003, DOI: 10.4401/ag-3429
Abstract: We present Continuous Wavelet Transform (CWT) data analysis of Virtual Geomagnetic Pole (VGP) latitude time series. The analyzed time series are sedimentary paleomagnetic and geodynamo simulated data. Two mother wavelets (the Morlet function and the first derivative of a Gaussian function) are used in order to detect features related to the spectral content as well as polarity excursions and reversals. By means of the Morlet wavelet, we estimate both the global spectrum and the time evolution of the spectral content of the paleomagnetic data series. Some peaks corresponding to the orbital components are revealed by the spectra and the local analysis helped disclose their statistical significance. Even if this feature could be an indication of orbital influence on geodynamo, other interpretations are possible. In particular, we note a correspondence of local spectral peaks with the appearance of the excursions in the series. The comparison among the paleomagnetic and simulated spectra shows a similarity in the high frequency region indicating that their degree of regularity is analogous. By means of Gaussian first derivative wavelet, reversals and excursions of polarity were sought. The analysis was performed first on the simulated data, to have a guide in understanding the features present in the more complex paleomagnetic data. Various excursions and reversals have been identified, despite of the prevalent normality of the series and its inherent noise. The found relative chronology of the paleomagnetic data reversals was compared with a coeval global polarity time scale (Channel et al., 1995). The relative lengths of polarity stability intervals are found similar, but a general shift appears between the two scales, that could be due to the datation uncertainties of the Hauterivian/Barremian boundary.
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