Literature DB >> 8523903

Real-time compression of myoelectric data utilising adaptive differential pulse code modulation.

J F Norris1, D F Lovely.   

Abstract

The myoelectric signal, obtained by either surface or needle electrodes, is used in many areas of clinical research and diagnosis. The conventional method of storing such information is in digitised form on a computer. However, the bandwidth of the signal and the required resolution result in large memory requirements. Adaptive differential pulse code modulation is investigated as a method of reducing the memory requirements for myoelectric data storage. In this scheme, a 12-bit sample is reduced to four bits, thus reducing the memory requirements by a factor of three. In reality, this compression ratio is closer to 4:1 owing to the fact that the widths of most memories are organised as multiples of eight bits.

Mesh:

Year:  1995        PMID: 8523903     DOI: 10.1007/bf02510779

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


  3 in total

1.  EEG data compression with source coding techniques.

Authors:  H Hinrichs
Journal:  J Biomed Eng       Date:  1991-09

2.  AZTEC, a preprocessing program for real-time ECG rhythm analysis.

Authors:  J R Cox; F M Nolle; H A Fozzard; G C Oliver
Journal:  IEEE Trans Biomed Eng       Date:  1968-04       Impact factor: 4.538

3.  Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity.

Authors:  F B Stulen; C J DeLuca
Journal:  IEEE Trans Biomed Eng       Date:  1981-07       Impact factor: 4.538

  3 in total
  5 in total

1.  Errors associated with the use of adaptive differential pulse code modulation in the compression of isometric and dynamic myo-electric signals.

Authors:  A D Chan; D F Lovely; B Hudgins
Journal:  Med Biol Eng Comput       Date:  1998-03       Impact factor: 2.602

2.  Compression of high-density EMG signals for trapezius and gastrocnemius muscles.

Authors:  Cinthia Itiki; Sergio S Furuie; Roberto Merletti
Journal:  Biomed Eng Online       Date:  2014-03-10       Impact factor: 2.819

3.  S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation.

Authors:  Marcel Henrique Trabuco; Marcus Vinícius Chaffim Costa; Francisco Assis de Oliveira Nascimento
Journal:  Biomed Eng Online       Date:  2014-02-27       Impact factor: 2.819

4.  SeisMote: A Multi-Sensor Wireless Platform for Cardiovascular Monitoring in Laboratory, Daily Life, and Telemedicine.

Authors:  Marco Di Rienzo; Giovannibattista Rizzo; Zeynep Melike Işılay; Prospero Lombardi
Journal:  Sensors (Basel)       Date:  2020-01-26       Impact factor: 3.576

5.  SEMG signal compression based on two-dimensional techniques.

Authors:  Wheidima Carneiro de Melo; Eddie Batista de Lima Filho; Waldir Sabino da Silva Júnior
Journal:  Biomed Eng Online       Date:  2016-04-18       Impact factor: 2.819

  5 in total

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