Development of ECG criteria to diagnose the number of narrowed coronary arteries in rest angina using new self-learning techniques

Abstract

Recently, an evaluation of the value of the resting electrocardiogram recorded during chest pain for identifying high-risk patients with three-vessel or left main stem coronary artery disease has resulted in the definition of one characteristic pattern: ST-segment depression in leads I, II, and V 4–V 6 and elevation in lead aVR. This study evaluated the generation of such criteria using two self-learning techniques: neural networks and induction algorithms. In 113 patients, five variables, including the amount of ST elevation, the number of leads with abnormal ST-segments, and this above-mentioned characteristic sign, were correlated with the number of narrowed vessels. All patients were randomly subdivided into a training (n = 63) and test set (n = 50), stratified for both this characteristic sign and for the vessel involved. Using the learning set, the neural network and the induction algorithm were trained separately to identify (1) pure left main stem disease and (2) three-vessel disease and left main stem disease. The neural network was trained for 1,000 runs. The induction algorithm was trained, allowing all variables to be used in any order. The experiments were repeated after adding weight factors to promote the recognition of the more severe cases. Subsequently, the ST elevation in all 12 leads was added to the training and test sets, once with and once without the polarity of the ST deviation. Altogether, 18 different combinations were evaluated. Basically, the neural network and the induction algorithm approach misclassified the same cases in corresponding test combinations. The application of weight factors either did not influence the classification or improved the results at the cost of the nonsupported category. The inclusion of the 12 additional parameters did not necessarily improve and sometimes dramatically worsened the classification process.