Name
Abstract | ||
|---|---|---|
A novel supervised neural network-based algorithm is designed to reliably distinguish in electrocardiographic (ECG) records between normal and ischemic beats of the same patient. The basic idea behind this paper is to consider an ECG digital recording of two consecutive R-wave segments (RRR interval) as a noisy sample of an underlying function to be approximated by a fixed number of Radial Basis Functions (RBF). The linear expansion coefficients of the RRR interval represent the input signal of a feed-forward neural network which classifies a single beat as normal or ischemic. The system has been evaluated using several patient records taken from the European ST-T database. Experimental results show that the proposed beat classifier is very reliable, and that it may be a useful practical tool for the automatic detection of ischemic episodes. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.amc.2009.03.013 | Applied Mathematics and Computation |
Keywords | Field | DocType |
electrocardiogram (ecg) beats,neural network classifier,radial basis functions,electrocardiogram beats,neural network,feed forward neural network,radial basis function | Mathematical optimization,Radial basis function,Pattern recognition,Neural network classifier,Computer science,Digital recording,Algorithm,Artificial intelligence,Beat (music),Classifier (linguistics),Artificial neural network | Journal |
Volume | Issue | ISSN |
213 | 1 | Applied Mathematics and Computation |
Citations | PageRank | References |
5 | 0.48 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. De Gaetano | 1 | 10 | 1.56 |
| S. Panunzi | 2 | 28 | 6.02 |
| F. Rinaldi | 3 | 181 | 19.61 |
| A. Risi | 4 | 28 | 2.88 |
| M. Sciandrone | 5 | 335 | 29.01 |