Efficient Compression of QRS Complexes Using Hermite Expansion

  • Authors:

  • Aliaksei Sandryhaila;Samir Saba;Markus Puschel;Jelena Kovacevic

  • Affiliations:

  • Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh,;Cardiovascular Institute, University of Pittsburgh Medical Center, Pittsburgh,;Department of Computer Science, ETH Zurich, Switzerland;Departments of Biomedical Engineering and Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh,

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2012

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Abstract

We propose a novel algorithm for the compression of ECG signals, in particular QRS complexes. The algorithm is based on the expansion of signals with compact support into a basis of discrete Hermite functions. These functions can be constructed by sampling continuous Hermite functions at specific sampling points. They form an orthogonal basis in the underlying signal space. The proposed algorithm relies on the theory of signal models based on orthogonal polynomials. We demonstrate that the constructed discrete Hermite functions have important advantages compared to continuous Hermite functions, which have previously been suggested for the compression of QRS complexes. Our algorithm achieves higher compression ratios compared with previously reported algorithms based on continuous Hermite functions, discrete Fourier, cosine, or wavelet transforms.