Literature DB >> 8558960

ANN compression of morphologically similar ECG complexes.

D J Hamilton1, D C Thomson, W A Sandham.   

Abstract

A compression algorithm for electrocardiogram signals is presented, based on an auto-associative neural network. Issues of weight and activation coding are considered, and compression performances of various network sizes are compared. A unique feature is the performance improvement achieved using DC level removal. A comparison with existing techniques is provided.

Mesh：

Year:  1995        PMID： 8558960     DOI： 10.1007/bf02523019

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN： 0140-0118            Impact factor:   2.602


  5 in total

1.  Compression of the ambulatory ECG by average beat subtraction and residual differencing.

Authors:  P S Hamilton; W J Tompkins
Journal:  IEEE Trans Biomed Eng       Date:  1991-03       Impact factor: 4.538

2.  Data compression of the ECG using neural network for digital Holter monitor.

Authors:  A Iwata; Y Nagasaka; N Suzumura
Journal:  IEEE Eng Med Biol Mag       Date:  1990

3.  A comparison of the noise sensitivity of nine QRS detection algorithms.

Authors:  G M Friesen; T C Jannett; M A Jadallah; S L Yates; S R Quint; H T Nagle
Journal:  IEEE Trans Biomed Eng       Date:  1990-01       Impact factor: 4.538

Review 4.  ECG data compression techniques--a unified approach.

Authors:  S M Jalaleddine; C G Hutchens; R D Strattan; W A Coberly
Journal:  IEEE Trans Biomed Eng       Date:  1990-04       Impact factor: 4.538

5.  ECG compression using long-term prediction.

Authors:  G Nave; A Cohen
Journal:  IEEE Trans Biomed Eng       Date:  1993-09       Impact factor: 4.538
  5 in total

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