scholarly journals Detection of Indeterminacies in Corrected ECG Signals Using Parameterized Multidimensional Independent Component Analysis

2009 ◽  
Vol 10 (2) ◽  
pp. 85-115 ◽  
Author(s):  
M. P. S. Chawla
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Complex Signals ◽  
Data Set ◽  
Noise Component ◽  
Ecg Signals ◽  
Independent Components ◽  
Statistical Measures ◽  
Ecg Data

Independent component analysis (ICA) is a new technique suitable for separating independent components from electrocardiogram (ECG) complex signals. The basic idea of using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces and to decompose each rotation into elementary rotations within all two-dimensional planes spanned by the coordinate axes useful for diagnostic information of heart. In this paper, ability of ICA for parameterization of ECG signals was felt to reduce the amount of redundant ECG data. This work aims at finding an independent subspace analysis (ISA) model for ECG analysis that allows applicability to any random vectors available in an ECG data set. For the common standards for electrocardiography (CSE) based ECG data sets, joint approximate diagonalization of eigen matrices (Jade) algorithm is used to find smaller subspaces. The extracted independent components are further cleaned by statistical measures. In this study, it is also observed that the value of kurtosis coefficients for the independent components, which represents the noise component, can be further reduced using parameterized multidimensional ICA (PMICA) technique. The indeterminacies if available in the ECG data are to be analysed also using modified version of Jade algorithm to PMICA and parameterized standard ICA (PsICA) for comparative studies. The indeterminacies if available in the ECG data are reduced in PMICA better in comparison to the analysis done using PsICA. The simulation results obtained indicate that ICA definitely improves signal–noise ratio (SNR) like the other higher order digital filtering methods like Kalman, Butterworth etc. with minimum reconstruction errors. Here, it is also confirmed that re-parameterization of the standard ICA model results into a ‘component model’ using MICA technique, which is geometric in spirit and free of indeterminacies existing in sICA model.

MULTIDIMENSIONAL INDEPENDENT COMPONENT ANALYSIS FOR STATISTICAL ESTIMATIONS OF INDETERMINACIES IN ELECTROCARDIOGRAMS

2009 ◽  
Vol 09 (03) ◽  
pp. 345-375 ◽  
Author(s):  
M. P. S. CHAWLA
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Set ◽  
Noise Component ◽  
Ecg Signals ◽  
Higher Dimensional ◽  
Source Signal ◽  
Statistical Estimations ◽  
Ecg Data

Independent component analysis (ICA) is a technique capable of separating independent components (ICs) from complex electrocardiogram (ECG) signals. The basic intention behind using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces. This study highlights the ability of ICA for parametrization of ECG signals to reduce the amount of redundant ECG data if any in a data set. The aim of this paper is to justify the underlying theory of the use of ICA and how it can be extended to for MICA separation of the ECG signals for combinational leads to attain most useful diagnostic information, which was not discussed in other some similar previous publications in this field. It is also investigated that the value of kurtosis coefficients for the ICs, which represents the noise component, can be further reduced using parametrized multidimensional independent component analysis (PMICA) technique. The indeterminacies available in the ECG data are also analyzed using modified version of Jade algorithm for PMICA and parametrized standard independent component analysis (PSICA). For the ECG data set, Jade algorithm is applied first to find smaller subspaces for MICA analysis and can therefore be regarded as a basis algorithm for PMICA analysis. The simulation results are obtained in Matlab environment to indicate that, ICA can definitely improve signal–noise ratio (SNR) in minimizing the reconstruction errors. The future scope of MICA expected by author is that, by reconsidering the notion of ICA, a more general perspective can be envisioned: i.e. modified multidimensional independent component analysis (MMICA). It would be based on a morphological geometric parametrization (MGP) which would further reduce the indeterminacies involved in matrix-based modeling (MBM).

scholarly journals Parameterization and R-Peak Error Estimations of ECG Signals Using Independent Component Analysis

2007 ◽  
Vol 8 (4) ◽  
pp. 263-285 ◽  
Author(s):  
M. P. S. Chawla
Keyword(s):  
Independent Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Percentage Error ◽  
Variance Estimator ◽  
Quadratic Spline ◽  
Ecg Signals ◽  
Statistical Measures ◽  
Ecg Data

Principal component analysis (PCA) is used to reduce dimensionality of electrocardiogram (ECG) data prior to performing independent component analysis (ICA). A newly developed PCA variance estimator by the author has been applied for detecting true, actual and false peaks of ECG data files. In this paper, it is felt that the ability of ICA is also checked for parameterization of ECG signals, which is necessary at times. Independent components (ICs) of properly parameterized ECG signals are more readily interpretable than the measurements themselves, or their ICs. The original ECG recordings and the samples are corrected by statistical measures to estimate the noise statistics of ECG signals and find the reconstruction errors. The capability of ICA is justified by finding the true, false and actual peaks of around 25–50, CSE (common standards for electrocardiography) database ECG files. In the present work, joint approximation for diagonalization of the eigen matrices (Jade) algorithm is applied to 3-channel ECG. ICA processing of different cases is dealt with and the R-peak magnitudes of the ECG waveforms before and after applying ICA are found and marked. ICA results obtained indicate that in most of the cases, the percentage error in reconstruction is very small. The developed PCA variance estimator along with the quadratic spline wavelet gave a sensitivity of 97.47% before applying ICA and 98.07% after ICA processing.

PARAMETRIZATION AND CORRECTION OF ELECTROCARDIOGRAM SIGNALS USING INDEPENDENT COMPONENT ANALYSIS

2007 ◽  
Vol 07 (04) ◽  
pp. 355-379 ◽  
Author(s):  
M. P. S. CHAWLA
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Reconstruction Error ◽  
Independent Component ◽  
Diagnostic Feature ◽  
Frequency Noise ◽  
Noise Power ◽  
Quadratic Spline ◽  
Ecg Signals ◽  
Ecg Data

Electrocardiogram (ECG) signals are largely employed as a diagnostic tool in clinical practice in order to assess the cardiac status of a specimen. Independent component analysis (ICA) of measured ECG signals yields the independent sources, provided that certain requirements are fulfilled. Properly parametrized ECG signals provide a better view of the extracted ECG signals, while reducing the amount of ECG data. Independent components (ICs) of parametrized ECG signals may also be more readily interpretable than original ECG measurements or even their ICs. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG signals for a clear interpretation of ECG data in diagnostic applications. In this work, ICA is tested on the Common Standards for Electrocardiography (CSE) database files corrupted by abrupt changes, high frequency noise, power line interference, etc. The joint approximation for diagonalization of eigen matrices (JADE) algorithm for ICA is applied to three-channel ECG, and the sources are separated as ICs. In this analysis, an extension is applied to the algorithm for further correction of the extracted components. The values of R-peak before and after application of ICA are found using quadratic spline wavelet, which facilitates the estimation of the reconstruction errors. The results indicate that, in most of the cases, the percentage reconstruction error is small at around 3%. The paper also highlights the advantages, limitations, and diagnostic feature extraction capability of ICA for clinicians and medical practitioners. Kurtosis is varied in the range of 3.0–7.0, and variance of variance (Varvar) is varied in the range of 0.2–0.5.

scholarly journals Robust Independent Component Analysis Based on Two Scatter Matrices

2016 ◽  
Vol 37 (1) ◽  
Author(s):  
Klaus Nordhausen ◽  
Hannu Oja ◽  
Esa Ollila
Keyword(s):  
Independent Component Analysis ◽  
Covariance Matrix ◽  
Simulation Study ◽  
Component Analysis ◽  
Independent Component ◽  
Scatter Matrix ◽  
Independent Components

Oja, Sirkiä, and Eriksson (2006) and Ollila, Oja, and Koivunen (2007) showed that, under general assumptions, any two scatter matrices with the so called independent components property can be used to estimate the unmixing matrix for the independent component analysis (ICA). The method is a generalization of Cardoso’s (Cardoso, 1989) FOBI estimate which uses the regular covariance matrix and a scatter matrix based on fourth moments. Different choices of the two scatter matrices are compared in a simulation study. Based on the study, we recommend always the use of two robust scatter matrices. For possible asymmetric independent components, symmetrized versions of the scatter matrix estimates should be used.

Independent component analysis for reservoir geomorphology and unsupervised seismic facies classification in the Taranaki Basin, New Zealand

2019 ◽  
Vol 7 (3) ◽  
pp. SE19-SE42 ◽  
Author(s):  
David Lubo-Robles ◽  
Kurt J. Marfurt
Keyword(s):  
New Zealand ◽  
Independent Component Analysis ◽  
Order Statistics ◽  
Input Data ◽  
Component Analysis ◽  
Independent Component ◽  
Seismic Facies ◽  
Blue Color ◽  
Independent Components ◽  
Facies Classification

During the past two decades, the number of volumetric seismic attributes has increased to the point at which interpreters are overwhelmed and cannot analyze all of the information that is available. Principal component analysis (PCA) is one of the best-known multivariate analysis techniques that decompose the input data into second-order statistics by maximizing the variance, thus obtaining mathematically uncorrelated components. Unfortunately, projecting the information in the multiple input data volumes onto an orthogonal basis often mixes rather than separates geologic features of interest. To address this issue, we have implemented and evaluated a relatively new unsupervised multiattribute analysis technique called independent component analysis (ICA), which is based on higher order statistics. We evaluate our algorithm to study the internal architecture of turbiditic channel complexes present in the Moki A sands Formation, Taranaki Basin, New Zealand. We input 12 spectral magnitude components ranging from 25 to 80 Hz into the ICA algorithm and we plot 3 of the resulting independent components against a red-green-blue color scheme to generate a single volume in which the colored independent components correspond to different seismic facies. The results obtained using ICA proved to be superior to those obtained using PCA. Specifically, ICA provides improved resolution and separates geologic features from noise. Moreover, with ICA, we can geologically analyze the different seismic facies and relate them to sand- and mud-prone seismic facies associated with axial and off-axis deposition and cut-and-fill architectures.

SIMPLE NEURON MODELS FOR INDEPENDENT COMPONENT ANALYSIS

1996 ◽  
Vol 07 (06) ◽  
pp. 671-687 ◽  
Author(s):  
AAPO HYVÄRINEN ◽  
ERKKI OJA
Keyword(s):  
Independent Component Analysis ◽  
Negative Feedback ◽  
Single Neuron ◽  
Component Analysis ◽  
Point Of View ◽  
Independent Component ◽  
Neuron Models ◽  
Independent Components ◽  
Unit System ◽  
Learning Rules

Recently, several neural algorithms have been introduced for Independent Component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbian-like learning rules are introduced for estimating one of the independent components from sphered data. Some of the learning rules can be used to estimate an independent component which has a negative kurtosis, and the others estimate a component of positive kurtosis. Next, a two-unit system is introduced to estimate an independent component of any kurtosis. The results are then generalized to estimate independent components from non-sphered (raw) mixtures. To separate several independent components, a system of several neurons with linear negative feedback is used. The convergence of the learning rules is rigorously proven without any unnecessary hypotheses on the distributions of the independent components.

Application of Fault Diagnosis Method Based on cICA to Gearbox

2014 ◽  
Vol 664 ◽  
pp. 148-152
Author(s):  
Shuang Xi Jing ◽  
Song Tao Guo ◽  
Jun Fa Leng ◽  
Xing Yu Zhao
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Signal To Noise Ratio ◽  
Component Analysis ◽  
Independent Component ◽  
Early Application ◽  
Independent Components ◽  
Gear Fault ◽  
Constrained Independent Component Analysis ◽  
Non Gaussian

Constrained independent component analysis (cICA) is a new theory and new method derived from the independent component analysis (ICA).It can extract the desired independent components (ICs) from the data based on some prior information, thus overcoming the uncertainty of the traditional ICA. Early gearbox fault signals is often very weak ,characterized by non-Gaussian,low signal-to-noise ratio (SNR), which make the existing diagnosis methods in the diagnosis of early application restricted. In this paper,cICA algorithm is applied to gear fault diagnosis. Through the case studies verify the feasibility of this method to extract the desired independent components (ICs), indicating the applicability and effectiveness of the method.

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