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 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.

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).

ECG signals denoising using wavelet transform and independent component analysis

2015 ◽  
Author(s):  
Manjin Liu ◽  
Mei Hui ◽  
Ming Liu ◽  
Liquan Dong ◽  
Zhu Zhao ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Ecg Signals

SINGLE-TRIAL BASED INDEPENDENT COMPONENT ANALYSIS ON MISMATCH NEGATIVITY IN CHILDREN

2010 ◽  
Vol 20 (04) ◽  
pp. 279-292 ◽  
Author(s):  
FENGYU CONG ◽  
IGOR KALYAKIN ◽  
TIINA HUTTUNEN-SCOTT ◽  
HONG LI ◽  
HEIKKI LYYTINEN ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Digital Filter ◽  
Mismatch Negativity ◽  
Low Frequency ◽  
Component Analysis ◽  
Event Related Potential ◽  
Independent Component ◽  
Frequency Noise ◽  
Theoretical Expectation ◽  
Single Trial

Independent component analysis (ICA) does not follow the superposition rule. This motivates us to study a negative event-related potential — mismatch negativity (MMN) estimated by the single-trial based ICA (sICA) and averaged trace based ICA (aICA), respectively. To sICA, an optimal digital filter (ODF) was used to remove low-frequency noise. As a result, this study demonstrates that the performance of the sICA+ODF and aICA could be different. Moreover, MMN under sICA+ODF fits better with the theoretical expectation, i.e., larger deviant elicits larger MMN peak amplitude.

Sign in / Sign up

Export Citation Format

Share Document