Open AccessJournal Article
PCA-based noise reduction in ambulatory ECGs
TLDR
A method for identifying the optimal subset of PC as a function of input SNR and number of channels was proposed and achieved an SNR improvement of 0.95dB–1.92dB.Abstract:
PCA can be used for cleaning noisy ECGs. With this aim, ECG with artificial motion artifacts were generated by combining clean 8-channel ECG with noise signals. 8-channel PCA was applied and then inverted after selecting a subset of principal components (PC). Input and output of PCA filtering was compared by calculating the correlation coefficient and estimating the SNR. Above 0dB, the PC corresponding to highest variance gave best performance, below 0dB the best PC was the second highest or lower variance. When SNR decreased, PCA performed better when retaining more number of PCs (3 PCs for a SNR=10dB down to 6 out of 8 PC for SNR=−10dB). Reducing the number of input ECG channels did not yield to a significant difference when it was reduced from eight down to two. A method for identifying the optimal subset of PC as a function of input SNR and number of channels was proposed. This method achieved an SNR improvement of 0.95dB–1.92dB.read more
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References
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Reference EntryDOI
Principal Component Analysis
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
Journal ArticleDOI
Independent component analysis: algorithms and applications
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.
Journal ArticleDOI
Principal component analysis in ECG signal processing
TL;DR: Several ECG applications are reviewed where PCA techniques have been successfully employed, including data compression, ST-T segment analysis for the detection of myocardial ischemia and abnormalities in ventricular repolarization, extraction of atrial fibrillatory waves for detailed characterization of atrium fibrillation, and analysis of body surface potential maps.
Proceedings ArticleDOI
Adaptive reduction of motion artifact in the electrocardiogram
TL;DR: This paper presents initial results of a novel approach to reducing ECG motion artifact using electrode motion as the reference signal to an adaptive filter and the motion signal and shows that the induced motion artifact was reduced in all data sets.
Journal ArticleDOI
Comparing stress ECG enhancement algorithms
TL;DR: Some of the published ECG enhancing techniques to overcome the noise problems are reviewed, and their performance on stress ECG signals under adverse noise scenarios are compared and the filter bank-based ECG enhances algorithm is described.
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