• DocumentCode
    786763
  • Title

    A low-power network for on-line diagnosis of heart patients

  • Author

    Coggins, Richard ; Jabri, Marwan ; Flower, Barry ; Pickard, Stephen

  • Author_Institution
    Syst. Eng. & Design Autom. Lab., Sydney Univ., NSW, Australia
  • Volume
    15
  • Issue
    3
  • fYear
    1995
  • fDate
    6/1/1995 12:00:00 AM
  • Firstpage
    18
  • Lastpage
    25
  • Abstract
    Implantable cardioverter defibrillators detect and treat dangerous cardiac arrhythmias. Current ICDs, however, cannot distinguish between some potentially fatal arrhythmias and benign conditions. Our system classifies intracardiac electrograms to detect such arrhythmias and uses analog techniques to meet the strict power and area requirements of implantable systems. A robust neural network architecture reduces the impact of noise, drift, and offsets inherent in analog approaches
  • Keywords
    defibrillators; medical signal processing; neural nets; patient monitoring; patient treatment; pattern recognition; cardiac arrhythmias; drift; heart patients; implantable cardioverter defibrillators; low-power network; noise; offsets; on-line diagnosis; robust neural network; Circuits; Electric shock; Heart; Implants; Medical treatment; Morphology; Neural networks; Rhythm; Timing; Very large scale integration;
  • fLanguage
    English
  • Journal_Title
    Micro, IEEE
  • Publisher
    ieee
  • ISSN
    0272-1732
  • Type

    jour

  • DOI
    10.1109/40.387678
  • Filename
    387678