Cover Image
QR Code

Differentiation of beats of ventricular and sinus origin using a self-training neural NETWORK.

Despite advances in the computerized detection arrhythmias, arrhythmia recognition by morphological waveform analysis still poses a difficult problem. Artificial neural networks, computer algorithms that are self-trained by an analog of biological synaptic modification to perform pattern recognition...

Full description

Published in:Pacing & Clinical Electrophysiology Vol. 17; no. 4; pp. 611 - 627
Main Authors: Evans SJ, Hastings H, Bodenheimer MM
Format: Journal Article
Published: Wiley-Blackwell Apr1994
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=106039823&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 106039823
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        01478389
        4F8
      jtl: Pacing & Clinical Electrophysiology
      issn: 01478389
      maglogo: Y
    pubinfo:
      dt: Apr1994
      vid: 17
      iid: 4
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
    artinfo:
      ui:
        106039823
        2009417191
        NLM7516545
        106039823
      ppf: 611
      ppct: 16
      formats:
        fmt:
          @attributes:
            type: P
      tig:
        atl: Differentiation of beats of ventricular and sinus origin using a self-training neural NETWORK.
      aug:
        au:
          Evans SJ
          Hastings H
          Bodenheimer MM
      sug:
      ab: Despite advances in the computerized detection arrhythmias, arrhythmia recognition by morphological waveform analysis still poses a difficult problem. Artificial neural networks, computer algorithms that are self-trained by an analog of biological synaptic modification to perform pattern recognition, hold great promise for the differentiation of various cardiac rhythms. The goal of this study was to differentiate beats of sinus and ventricular origin on a global basis and on a patient-specific basis by the use of artificial neural network analysis. Neural networks were trained to recognize digitized intracardiac electrograms (9 patients) and surface electrocardiograms (11 patients) obtained during sinus rhythm and ventricular tachycardia. After training, sinus rhythm or ventricular tachycardia beats were input into the neural network, and classified as to their origin. By the use of modified receiver operating characteristic curve plots, it was possible to differentiate with high sensitivity and specificity between beats of sinus origin and ventricular origin in all patients. The addition of high amounts of noise to the beats did not markedly degrade the performance of the surface ECG neural networks, and still allowed high sensitivity in differentiating beats of sinus origin from beats of ventricular origin, especially when noise was added to the training set. Neural networks provided sensitive and specific detection of cardiac electrical activity during sinus rhythm and ventricular tachycardia, and may play an important role & allowing development of improved arrhythmia recognition and management systems.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N