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

Content Provider	IEEE Xplore Digital Library
Author	Dorffner, G. Leitgeb, E. Koller, H.
Copyright Year	1994
Description	Author affiliation: Austrian Res. Inst. for Artificial Intelligence, Vienna, Austria (Dorffner, G.)
Abstract	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.
Starting Page	499
Ending Page	508
File Size	501382
Page Count	10
File Format	PDF
ISBN	0780320263
DOI	10.1109/NNSP.1994.366016
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-09-06
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrocardiography Cardiac disease Neural networks Testing Coronary arteriosclerosis Demography Stress measurement Time measurement Recurrent neural networks Feedforward neural networks
Content Type	Text
Resource Type	Article

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