Use of artificial neural networks within deterministic logic for the computer ECG diagnosis of inferior myocardial infarction.

An investigation into the use of software-based artificial neural networks for the electrocardiographic (ECG) detection of inferior myocardial infarction was made. A total of 592 clinically validated subjects, including 208 with inferior myocardial infarction, 300 normal subjects, and 84 left ventricular hypertrophy cases, were used in this study. A total of 200 ECGs (100 from patients with inferior myocardial infarction and 100 from normal subjects) were fed to 66 supervised feedforward neural networks for training using a back-propagation algorithm. QRS and ST-T wave measurements were used as the input parameters for the neural networks. The best performing network using QRS measurements only and the best using QRS and ST-T data were selected by assessing a test set of 292 ECGs (108 from patients with inferior myocardial infarction, 84 from patients with left ventricular hypertrophy, and 100 from normal subjects). These two networks were then implanted separately into the deterministic Glasgow program for further study. After the implementation, it was found necessary to include a small inferior Q criterion to improve the specificity of reporting inferior myocardial infarction, thereby producing a small loss of sensitivity as compared with use of the network alone. The use of an artificial neural network within the deterministic logic performed better than either alone in the diagnosis of inferior myocardial infarction, producing a 20% gain in sensitivity with 2% loss in overall specificity compared with the original deterministic logic.

[1]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[2]  E F Cook,et al.  A computer protocol to predict myocardial infarction in emergency department patients with chest pain. , 1988, The New England journal of medicine.

[3]  W. Baxt.  Use of an artificial neural network for the diagnosis of myocardial infarction. , 1991, Annals of internal medicine.

[4]  Jeffrey A. Stem,et al.  A computer-derived protocol to aid in the diagnosis of emergency room patients with acute chest pain. , 1982, The New England journal of medicine.

[5]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[6]  Giovanni Bortolan,et al.  Neural networks for ECG classification , 1990, [1990] Proceedings Computers in Cardiology.

[7]  P W Macfarlane,et al.  Neural networks for classification of ECG ST-T segments. , 1992, Journal of electrocardiology.

[8]  J. Stechmiller,et al.  Prognostic Efficacy of Early Clinical Categorization of Myocardial Infarction Patients , 1977, Circulation.

[9]  W. Dassen,et al.  An Artificial Neural Network to Localize Atrioventricular Accessory Pathways in Patients Suffering from the Wolff‐Parkinson‐White Syndrome , 1990, Pacing and clinical electrophysiology : PACE.

[10]  P W Macfarlane,et al.  Methodology of ECG Interpretation in the Glasgow Program , 1990, Methods of Information in Medicine.

[11]  Y. Nagasaka,et al.  Data compression of the ECG using neural network for digital Holter monitor , 1990, IEEE Engineering in Medicine and Biology Magazine.

[12]  R B D'Agostino,et al.  The usefulness of a predictive instrument to reduce inappropriate admissions to the coronary care unit. , 1980, Annals of internal medicine.