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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Froning, J.N. Brotherton, T. Simpson, P. Froelicher, V.F. Do, D. |
| Copyright Year | 1994 |
| Description | Author affiliation: Sunnyside Biomedical, USA (Froning, J.N.) |
| Abstract | A hierarchical fuzzy neural-net approach has been developed to classify averaged serial ECG waveforms gathered during exercise testing in order to determine the severity of coronary artery disease (CAD). The ST-T complex of each ECG was first transformed on a sample-by-sample basis to form multiple representations (e.g. raw amplitudes, delta-baseline, slope through J-junction) to highlight the salient features of the signal. These representations were then initially classified by individual neural-nets and the resultant CAD-group outputs used as inputs into a secondary fusion-net for final classification. Also at the fusion-net level, additional parameters (e.g., HR and phase) were added to the fusion-net's input dimension space. Using only one lead, the processing gives nearly perfectly discrimination between angiographic normals and patients with severe 3-vessel disease. The use of FMM neural-nets is particularly relevant for this type of medical application since they allow the user to determine why and where the network decided on its results.< |
| Starting Page | 605 |
| Ending Page | 608 |
| File Size | 367448 |
| Page Count | 4 |
| File Format | |
| ISBN | 081866570X |
| DOI | 10.1109/CIC.1994.470119 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-09-25 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Electrocardiography Testing Diseases Coronary arteriosclerosis Heart rate Biomedical measurements Manuals Diversity reception Image analysis |
| Content Type | Text |
| Resource Type | Article |
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