DEVELOPMENT OF ECG CRITERIA TO DIAGNOSE THE NUMBER OF NARROWED CORONARY-ARTERIES IN REST ANGINA USING NEW SELF-LEARNING TECHNIQUES

Recently, an evaluation of the value of the resting electrocardiogram recorded during chest pain for identifying high-risk patients with thr ee-vessel or left main stem coronary artery disease has resulted in th e definition of one characteristic pattern: ST-segment depression in l eads I, II, and V-4-V-6 and elevation in lead aVR. This study evaluate d the generation of such criteria using two self-learning techniques: neural networks and induction algorithms. In 113 patients, five variab les, including the amount of ST elevation, the number of leads with ab normal ST-segments, and this above-mentioned characteristic sign, were correlated with the number of narrowed vessels. All patients were ran domly subdivided into a training (n = 63) and test set (n = 50), strat ified for both this characteristic sign and for the vessel involved. U sing the learning set, the neural network and the induction algorithm were trained separately to identify (1) pure left main stem disease an d (2) three-vessel disease and left main stem disease. The neural netw ork was trained for 1,000 runs. The induction algorithm was trained, a llowing all variables to be used in any order. The experiments were re peated after adding weight factors to promote the recognition of the m ore severe cases. Subsequently, the ST elevation in all 12 leads was a dded to the training and test sets, once with and once without the pol arity of the ST deviation. Altogether, 18 different combinations were evaluated. Basically, the neural network and the induction algorithm a pproach misclassified the same cases in corresponding test combination s. The application of weight factors either did not influence the clas sification or improved the results at the cost of the nonsupported cat egory. The inclusion of the 12 additional parameters did not necessari ly improve and sometimes dramatically worsened the classification proc ess.