Document Detail


Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks.
MedLine Citation:
PMID:  9323064     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: The 12-lead ECG, together with patient history and clinical findings, remains the most important method for early diagnosis of acute myocardial infarction. Automated interpretation of ECG is widely used as decision support for less experienced physicians. Recent reports have demonstrated that artificial neural networks can be used to improve selected aspects of conventional rule-based interpretation programs. The purpose of this study was to detect acute myocardial infarction in the 12-lead ECG with artificial neural networks. METHODS AND RESULTS: A total of 1120 ECGs from patients with acute myocardial infarction and 10,452 control ECGs, recorded at an emergency department with computerized ECGs, were studied. Artificial neural networks were trained to detect acute myocardial infarction by use of measurements from the 12 ST-T segments of each ECG, together with the correct diagnosis. After this training process, the performance of the neural networks was compared with that of a widely used ECG interpretation program and the classification of an experienced cardiologist. The neural networks showed higher sensitivities and discriminant power than both the interpretation program and cardiologist. The sensitivity of the neural networks was 15.5% (95% confidence interval [CI], 12.4 to 18.6) higher than that of the interpretation program compared at a specificity of 95.4% (P<.00001) and 10.5% (95% CI, 7.2 to 13.6) higher than the cardiologist at a specificity of 86.3% (P<.00001). CONCLUSIONS: Artificial neural networks can be used to improve automated ECG interpretation for acute myocardial infarction. The networks may be useful as decision support even for the experienced ECG readers.
Authors:
B Hedén; H Ohlin; R Rittner; L Edenbrandt
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Circulation     Volume:  96     ISSN:  0009-7322     ISO Abbreviation:  Circulation     Publication Date:  1997 Sep 
Date Detail:
Created Date:  1997-10-22     Completed Date:  1997-10-22     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0147763     Medline TA:  Circulation     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  1798-802     Citation Subset:  AIM; IM    
Affiliation:
Department of Clinical Physiology, Lund University, Sweden.
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MeSH Terms
Descriptor/Qualifier:
Adult
Aged
Aged, 80 and over
Cardiology / instrumentation*,  methods
Electrocardiography / methods*
Humans
Middle Aged
Myocardial Infarction / diagnosis*
Neural Networks (Computer)*
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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