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.
Highlights
Principal component analysis (PCA) and independent component analysis (ICA) occupies a definite place in higher order statistical techniques for better feature extraction and electrocardiogram (ECG) interpretation [1,3]
ECG signals are largely employed as a diagnostic tool in clinical practice in order to assess the cardiac status of an object
Jade algorithm and quadratic spline wavelet along with PCA variance estimator gave a sensitivity of 97.47% before applying ICA and 98.07% after ICA processing to several CSE based ECG data files
Summary
Principal component analysis (PCA) and independent component analysis (ICA) occupies a definite place in higher order statistical techniques for better feature extraction and electrocardiogram (ECG) interpretation [1,3]. JKurtj and Varvar denote respectively the modulus of kurtosis and variance of variance as first proposed in [1] and used as an application in this study It is verified and reimplemented in this analysis that the independent components whose jKurtjkKurt (Threshold) and VarvarlVarvar (Threshold) is taken as a noise or artifact component [2 – 4,31]. This study is a reimplementation of [1] as far as thresholds for kurtosis and variance of variance (Varvar) are concerned It is confirmed after various case studies that ICA yields Independent Components (ICs) displaying more clearly the investigated properties of the original ECG sources. ICs of properly parameterized ECG signals [21] may be more readily interpretable than the measurements themselves, or their ICs. The applied ICA algorithm in this study estimates the independence of the original signal, and the optimization based on the estimation searches an optimum-restoring matrix. In the proposed model, estimated results are in good agreement with the physiological view [4,9,10,21]
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