Literature DB >> 1402511

Variability in ECG computer interpretation. Analysis of individual complexes vs analysis of a representative complex.

J A Kors1, G van Herpen, J H van Bemmel.   

Abstract

Variability in the electrocardiogram (ECG) can be due to extrinsic noise or can be caused by intrinsic factors, such as changes in the volume conductor or in the heart itself. Computer programs for the interpretation of the ECG base their diagnostic classification on one set of measurements that is derived from a representative PQRST complex or that is computed by taking the median from the measurements for each complex in the recording. However, these methods may fail to do justice to the intrinsic variability that may be present in the ECG. An alternative method is proposed: derive a set of measurements from each complex in the recording, classify each individual complex separately, and then combine the individual classifications into one final classification. This procedure has been evaluated on a validated database (n = 1,220) using an ECG computer program. Total accuracy against the clinical evidence increased from 69.8% for the interpretations of the averaged complexes to 71.2% for the combined interpretations of the individual complexes (p < 0.001). The effect of beat-to-beat variation on the measurements and classifications is demonstrated and the influence of extrinsic and intrinsic variability is assessed.

Mesh:

Year:  1992        PMID: 1402511     DOI: 10.1016/0022-0736(92)90031-t

Source DB:  PubMed          Journal:  J Electrocardiol        ISSN: 0022-0736            Impact factor:   1.438


  3 in total

1.  Magnitude, mechanism, and reproducibility of QT interval differences between superimposed global and individual lead ECG complexes.

Authors:  Paul Kligfield; Benoit Tyl; Martine Maarek; Pierre Maison-Blanche
Journal:  Ann Noninvasive Electrocardiol       Date:  2007-04       Impact factor: 1.468

2.  Consensus development for healthcare professionals.

Authors:  Bory Kea; Benjamin Chih-An Sun
Journal:  Intern Emerg Med       Date:  2014-11-28       Impact factor: 3.397

3.  ECG data compression using a neural network model based on multi-objective optimization.

Authors:  Bo Zhang; Jiasheng Zhao; Xiao Chen; Jianhuang Wu
Journal:  PLoS One       Date:  2017-10-03       Impact factor: 3.240

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.