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Important ECG diagnosis‐aiding indices of ventricular septal defect children with or without congestive heart failure
Author(s) -
Guo Meihui,
Huang MongNa Lo,
Bai Zhidong,
Hsieh KaiSheng
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.748
Subject(s) - outlier , receiver operating characteristic , logistic regression , heart failure , statistics , pairwise comparison , pattern recognition (psychology) , medicine , computer science , cardiology , mathematics , artificial intelligence
In this paper we perform a statistical study of the conventional RR intervals and two newly defined PR ′ and RT intervals of ECG data. A quadratic classification rule is applied to extract several important ECG diagnosis‐aiding indices among normal children and children with ventricular septal defect (VSD) with or without congestive heart failure (CHF). The results show that certain statistics computed from PR ′, RR and RT intervals are important diagnosis‐aiding indices. Best classification vectors are searched for pairwise classification. Two methods, minimum distance criterion and a two‐stage classification procedure, are considered for three‐way classification. Furthermore, logistic regression models based on transformations of these important diagnosis‐aiding indices are proposed. The receiver operating characteristic curves of the proposed models show better performance than those of linear and quadratic logistic models. In order to proceed with this study, a computer algorithm to automatically detect the three intervals is developed and the related ECG data are collected and analysed. The algorithm is also enhanced with an outlier detection procedure for the automatic measurements of the PR ′ and RT intervals. Copyright © 2001 John Wiley & Sons, Ltd.