NDLI: Decision fusion of micro-variability and signal averaged ECG parameters from the QRS complex with RBF networks

Content Provider	IEEE Xplore Digital Library
Author	Kestler, H.A. Muller, A. Hombach, V. Wohrle, J. Grebe, O. Palm, G. Moher, M. Schwenker, F.
Copyright Year	2002
Description	Author affiliation: Neural Inf. Process., Ulm Univ., Germany (Kestler, H.A.; Muller, A.)
Abstract	Two types of measurements are usually performed from high resolution ECG recordings: (a) static parameters derived from the signal-averaged QRS complex and (b) variant markers derived from beat-to-beat recordings. It is known that an increased QRS micro-variability and ventricular late potentials are associated with an increased risk for malignant arrhythmias. However, the diagnostic power of the singular parameters is limited In this study we investigated the diagnostic ability of a decision fusion of both variant and static high-resolution ECG parameters with radial-basis-function (RBF) networks. Continuous and signal-averaged ECGs were recorded from 51 healthy volunteers without any structural heart disease and no cardiac risk factors and from 44 patients with coronary heart disease and ventricular arrhythmias. Beat-to-beat micro-variability measurement of the QRS complex and the ST-T segment was based on 250 consecutive sinus beats per individual. Signal-averaged ECGs were analyzed with the Simson method (QRSD, RMS, LAS). Two RBF networks were trained One on the three signal averaged parameters and one with the 141D variability vector The two soft decisions from each RBF network were then combined by average fusion and maximum detection into a final crisp decision which resulted in an unusually high discriminative accuracy.
Starting Page	297
Ending Page	300
File Size	393035
Page Count	4
File Format	PDF
ISBN	0780377354
ISSN	02766547
DOI	10.1109/CIC.2002.1166767
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-09-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrocardiography Radial basis function networks Cancer Cardiac disease Signal processing Cardiology Signal resolution Signal analysis Coronary arteriosclerosis Cardiovascular diseases
Content Type	Text
Resource Type	Article

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