Embedded filter bank-based algorithm for ECG compression

Blanco Velasco, Manuel and Cruz Roldán, Fernando and Moreno Martínez, Eduardo and Godino Llorente, Juan Ignacio and Barner, Kenneth E. (2008). Embedded filter bank-based algorithm for ECG compression. "Signal Processing", v. 88 (n. 6); pp. 1402-1412. ISSN 0165-1684.


Title: Embedded filter bank-based algorithm for ECG compression
  • Blanco Velasco, Manuel
  • Cruz Roldán, Fernando
  • Moreno Martínez, Eduardo
  • Godino Llorente, Juan Ignacio
  • Barner, Kenneth E.
Item Type: Article
Título de Revista/Publicación: Signal Processing
Date: June 2008
Volume: 88
Freetext Keywords: Electrocardiogram (ECG); ECG compression; Embedded zerotree wavelet; Wavelet packets (WP); Channel bank filter; Filtering theory; Filter bank
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Ingeniería de Circuitos y Sistemas [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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In this work, two ECG compression schemes are presented using two types of filter banks to decompose the incoming signal: wavelet packets (WP) and nearly-perfect reconstruction cosine modulated filter banks. The conventional embedded zerotree wavelet (EZW) algorithm takes advantage of the hierarchical relationship among subband coefficients of the pyramidal wavelet decomposition. Nevertheless, it performs worse when used with WP as the hierarchy becomes more complex. In order to address this problem, we propose a new technique that considers no relationship among coefficients, and is therefore suitable for use with WP. Furthermore, this new approximation makes it possible to apply the quantization method toM-channel maximally decimated filter banks. In this fashion, the proposed algorithm provides two efficient and effective ECG compressors that show better ECG compression performance than the conventional EZW algorithm.

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Item ID: 2303
DC Identifier: http://oa.upm.es/2303/
OAI Identifier: oai:oa.upm.es:2303
Deposited by: Memoria Investigacion
Deposited on: 21 May 2010 11:22
Last Modified: 20 Apr 2016 12:03
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