Principal Component Analysis (PCA)

Principal Component Analysis (PCA)

Principal component analysis (PCA) is a transformation which allows converting a set of observations possibly correlated into a set of values linearly uncorrelated called principal components.  The transformation is defined in such a way that the new orthogonal space exploits the variance of the data.  First principal component will have the largest possible variance; the second will have the highest variance under the constraint that it is orthogonal to the first component and so on.

Setup to compute principal component analysis (PCA).Setup to compute principal component analysis (PCA).



The new basis can be generated based on: Time interval; Events; and percentage of variance.

  • Time interval: a piece of the signal will be used to generate the new eigenvectors and eigenvalues to project the original data.
  • Events: instead of a piece of signal, several time intervals corresponding with a certain characteristic (for instance, T wave interval) can be used to generate a transformation which exploits the similarity with the selected basis.
  • Variance: A percentage of variance is selected and then the algorithm generates the maximum number of new principal components that fits that variance.

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