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Neural network versus electrocardiographer and conventional computer criteria in diagnosing anterior infarct from the ECG
, L. Edenbrandt, J. Svensson, W.K. Haisty, O. Pahlm
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 667 - 670
Artificial neural networks have recently been introduced in the field of computer-based analysis of the electrocardiogram (ECG). The purpose of the present study was to examine the performance of a neural network in an ECG classification task. ECGs recorded from 272 patients with anterior myocardial infarction and 479 subjects without myocardial infarction were studied. Fifteen QRS measurements of the leads V2-V4 were used as inputs to the network. The network was trained using 502 ECGs. Thereafter, a comparison of network, conventional criteria and human expert was performed using a test set of249 ECGs. The neural network showed a higher sensitivity than the conventional criteria (79% versus 68%, p<0.01), both having a specificity of 97%. The performance of the human expert was the same as that of the neural network. In conclusion, it seems possible that neural networks could be used to improve the performance of some parts of ECG interpretation programs. © 1992 IEEE.
About the journal
JournalData powered by TypesetProceedings - Computers in Cardiology, CIC 1992
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.