Key result
Multiscale PCA applied to multichannel ECG signals successfully reduced noise while preserving all clinical components and required PQRST morphologies.
Multiscale PCA provides an effective method for denoising multichannel ECG signals while preserving essential clinical morphological features.
May aid ECG denoising in research settings; leaves open clinical adoption pending validation.
In this article, it is suggested to apply Multiscale PCA to multichannel ECG signals, for quality controlled denoising. PCA is applied at wavelet scales after forming multivariate data matrices. The quality controlled denoising is a two steps process, (a) selection of multivariate matrices at wavelet scale for PCA based dimension reduction and (b) the choice of number of Principal Component (PC) at each wavelet matrices. The selection of effective PCs is normally based on the cumulative percentage of total variation shown by eigenvalues. For conventional PCA, it is taken 70% to 99% [3]. The diagnostic quality of denoised signal is qualitatively compared with the original signal. The quantitative distortion measures like PRD and WEDD are evaluated [10], [12], [13]. The enhanced multichannel signals contain all the clinical components with required 'PQRST' morphologies of ECG signals while reducing the noise.
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L. N. Sharma (2012) studied ECG signal denoising. Multiscale PCA based Quality Controlled Denoising vs. Original signal was evaluated on Diagnostic quality and quantitative distortion measures (PRD and WEDD). Multiscale PCA applied to multichannel ECG signals successfully reduced noise while preserving all clinical components and required PQRST morphologies.
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