Parameterization and R‐Peak Error Estimations of ECG Signals Using Independent Component Analysis

Abstract

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.

Independent Component Analysis in ECG Signal Processing

“…By ECG amplitude parameterization, we mean construction of a new set of signals from the signal amplitudes at some defined fiducial points of the ECG, such as R peak or ST60 amplitudes (amplitudes 60 ms after the start of the ST segments), or from time averages of delineated ECG segments. ICA of parameterized ECG has been proposed, e.g., by Chawla (2007) and Tanskanen et al (2006aTanskanen et al ( , 2006b. In this Section, we explicitly show that such ECG signal parameterizations in fact fulfill the assumption of linearly combined components.…”

“…ECG based diagnostics applications in which ICA has been utilized include, e.g., classification of ECG beats (Chou & Yu, 2007;Huang, et al, 2010), analysis of parameterized ECG signals (Chawla, 2007;Tanskanen et al, 2006aTanskanen et al, , 2006b, heart rate variability analysis (Zhangyong et al, 2005), arrhythmia estimation (Castells et al, 2005;Jiang et al, 2006;Llinares & Igual, 2009), and atrial fibrillation extraction and analysis (Rieta et al, 2004;Stridh & Sörnmo, 2001;Zarzoso & Comon, 2010). A nice diagram of an atrial source separation system has been presented by Castells et al (2005).…”

A comparative analysis of principal component and independent component techniques for electrocardiograms

“…Moreover, the results are as accurate as those obtained with other methods, but with much less effort. The disadvantage of this method is the need to calculate some approximations to find the best one, although it is fast enough in any computer [1,5,7,8,10]. PCA is one of the most established techniques in multivariate statistical analysis and has been applied to ECG compression.…”

“…heart [1,2,4,5,[7][8][9]. The ECG relates the observed ionic current on the skin to events that occur in heart.…”

“…The extraction of independent components (ICs) is done using ICA, and various combinational ECG lead cases among the leads V1, V3, V6, AF, AR and AL are checked to find correlations between them for best feature extraction and classification. Variance is calculated for ten segments considered for each of the IC and then total variance of these variances is carried out to reveal all the hidden dynamics in the ECG signals, as first discussed in [6] and then reimplemented in [1,5,7,8] as a mark of check of the results presented in [6]. The simulation results are obtained for CSE database ECG signals using Matlab.…”