Literature DB >> 8288278

ECG compression using long-term prediction.

G Nave1, A Cohen.   

Abstract

A new algorithm for ECG signal compression is introduced. The compression system is based on the subautoregression (SAR) model, known also as the long-term prediction (LTP) model. The "periodicity" of the ECG signal is employed in order to further reduce redundancy, thus yielding high compression ratios. The suggested algorithm was evaluated using an in-house database. Very low bit rates on the order of 70 b/s are achieved with a relatively low reconstruction error (percent rms difference-PRD) of less than 10%. The algorithm was compared, using the same database, with the conventional linear prediction (short-term prediction--STP) method, and was found superior at any bit rate. The suggested algorithm can be considered a generalization of the recently published average beat subtraction method.

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Year:  1993        PMID: 8288278     DOI: 10.1109/10.245608

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  5 in total

1.  A chaos-based model for low complexity predictive coding scheme for compression and transmission of electroencephalogram data.

Authors:  V Kavitha; D N Dutt
Journal:  Med Biol Eng Comput       Date:  1999-05       Impact factor: 2.602

2.  Adaptive vector quantisation for electrocardiogram signal compression using overlapped and linearly shifted codevectors.

Authors:  S G Miaou; J H Larn
Journal:  Med Biol Eng Comput       Date:  2000-09       Impact factor: 2.602

3.  ECG compression by modelling the instantaneous module/phase of its DCT.

Authors:  Jean-Claude Nunes; Amine Nait-Ali
Journal:  J Clin Monit Comput       Date:  2005-06       Impact factor: 2.502

4.  ANN compression of morphologically similar ECG complexes.

Authors:  D J Hamilton; D C Thomson; W A Sandham
Journal:  Med Biol Eng Comput       Date:  1995-11       Impact factor: 2.602

5.  Redundancy cancellation of compressed measurements by QRS complex alignment.

Authors:  Fahimeh Nasimi; Mohammad Reza Khayyambashi; Naser Movahhedinia
Journal:  PLoS One       Date:  2022-02-08       Impact factor: 3.240

  5 in total

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