Neural network versus electrocardiographer and conventional computer criteria in diagnosing anterior infarct from the ECG

Proceedings - Computers in Cardiology, CIC 1992

Volumn , Issue , 1992, Pages 667-670

  Reddy, M R S ^a   Edenbrandt, L ^a   Svensson, J ^a   Haisty, W K ^b   Pahlm, O ^a  

^a LUND UNIVERSITY   (Sweden)

^b WAKE FOREST BAPTIST HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIOLOGY; COMPUTER AIDED ANALYSIS; ELECTROCARDIOGRAPHY; NEURAL NETWORKS;

COMPUTER-BASED ANALYSIS; CONVENTIONAL COMPUTERS; ECG CLASSIFICATIONS; ECG INTERPRETATION; HUMAN EXPERT; MEASUREMENTS OF; MYOCARDIAL INFARCTION; TEST SETS;

COMPUTER NETWORKS;

EID: 0345105562     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIC.1992.269345     Document Type: Conference Paper

References (7)

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2. Edenbrandt L, Devine B, Macfarlane PW. Neural networks for classification of electrocardiographic ST-T segments. J Electrocardiol 1992;25:167-173.
3. The CSE Working Party. Recommendations for measurement standards in quantitative electrocardiography. Eur Heart J 1985:6;815-825.
4. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, eds. Parallel Distributed Processing, Vol 1. Cambridge, MA: MIT Press, 1986:318-362.
5. Macfarlane PW, Lawrie TDV. Comprehensive Electrocardiology. Vol 3. Oxford: Pergamon Press Inc., 1989:1543-1552.
6. Willems JL, Abreu-Lima C, Arnaud P et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N ENGL J Med 1991;325:1767-1773.
7. Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med 1991;115:843-848.