Important ECG diagnosis-aiding indices of ventricular septal defect children with or without congestive heart failure.
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.
Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung, Taiwan. guomh@math.nsysu.edu.tw
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M
Guo
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MN
Huang
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Bai
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Hsieh
D000465:Algorithms; D002648:Child; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data; D003937:Diagnosis, Differential; D004562:Electrocardiography / Q000145:classification / Q000706:statistics & numerical data*; D005260:Female; D006333:Heart Failure / Q000175:diagnosis* / Q000453:epidemiology; D006345:Heart Septal Defects, Ventricular / Q000175:diagnosis* / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models; D008297:Male; D012372:ROC Curve; D012016:Reference Values; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted