• DocumentCode
    3206157
  • Title

    Vector quantization neural network for ECG signal compression

  • Author

    Bhatt, Nishith S. ; Shah, Satish K.

  • Author_Institution
    Comput. Eng. Dept., Sarvajanik Coll. of Eng. & Technol., Surat, India
  • Volume
    1
  • fYear
    2002
  • fDate
    28-31 Oct. 2002
  • Firstpage
    625
  • Abstract
    Better compression results can be achieved by coding vectors instead of scalars. The proposed algorithm is used for competitive learning of a vector quantization neural network, which is used to generate a codebook for the vector quantization of ECG signals. The competitive learning algorithm can successfully group the sample group to generate the codebook and reproduce at the time of reconstruction.
  • Keywords
    adaptive decoding; electrocardiography; medical signal processing; neural nets; signal reconstruction; table lookup; unsupervised learning; vector quantisation; ECG; codebook generation; competitive learning; compression ratio; decoder; percent RMS difference; reconstruction; sample group; signal compression; vector coding; vector quantization neural network; Data compression; Decoding; Educational institutions; Electrocardiography; Heart; Neural networks; Parameter extraction; Postal services; Signal generators; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering
  • Print_ISBN
    0-7803-7490-8
  • Type

    conf

  • DOI
    10.1109/TENCON.2002.1181352
  • Filename
    1181352
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3206157