Toward improving exercise ECG for detecting ischemic heart disease with recurrent and feedforward neural nets

This paper reports about a study evaluating the usefulness of neural networks for the early detection of heart disease based on ECG and other measurements during exercise testing. Data from 350 persons who underwent stress tests consisted of patient demographic data and fifteen time frames of measurements during stress and rest. Three different neural networks, two recurrent and one feedforward using background knowledge for preprocessing, were trained and compared to the performance of skilled cardiologists. It could be shown that the best neural networks can compete with experts in classifying tests as CAD (coronary artery disease) or normal. What concerns an index value expressing the likelihood of disease, to be used for monitoring the success of treatments, the neural networks outperformed classical statistical techniques published previously. This study has thus shown large evidence in favor of using neural nets to improve the exercise ECG as a noninvasive technique for detecting heart diseases