Myocardial infarction--pinpointing the key indicators in the 12-lead ECG using data mining.
In this paper we describe how data mining techniques were used in order to pinpoint the key indicators for myocardial infarction in the electrocardiogram (ECG) by determining existing trends in a large data set. In order to provide a test bed for the data mining techniques a data mining tool was developed so that the effectiveness of various data mining techniques could be determined. The material consisted of 2730 ECGs recorded at an emergency department. A total of 517 ECGs were recorded on patients suffering acute myocardial infarction. The remaining ECGs were defined as control ECGs. A subset of the material was used to train the data mining tool. After training, the data mining tool was able to pinpoint the key ECG indicators for myocardial infarction in the test set (duration and amplitude of the Q wave and R duration in lead V2) and successfully determine which patients had suffered a heart attack.
Burn-Thornton
K E
KE
SECEE, Plymouth University, Devon, United Kingdom.
Edenbrandt
L
L
D000465:Algorithms; D019992:Databases as Topic; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography; D006801:Humans; D016247:Information Storage and Retrieval; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000503:physiopathology; D015203:Reproducibility of Results