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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Heden, B. Edenbrandt, L. Haisty Jr., W.K. Pahlm, O. |
| Copyright Year | 1993 |
| Description | Author affiliation: Dept. of Clinical Physiol., Lund Univ., Sweden (Heden, B.; Edenbrandt, L.) |
| Abstract | Artificial neural networks are computer-based expert systems which have been used for several pattern recognition tasks, e.g. classification of electrocardiograms (ECG). Another method which has proved to be a valuable tool for improving the quality of ECG interpretation is the synthesized vectorcardiogram (VCG). These two methods were combined and applied on a material of 1458 ECGs recorded from subjects with or without inferior myocardial infarction (MI) evidences by ECG independent data. Neural networks fed with measurements from the ECG as well as the synthesized VCG showed after a learning process a sensitivity of 83% at a level of specificity of 97%. The result shows that planar ST-T measurements and QRS measurements from the synthesized VCG improved the performance compared to networks fed with scalar QRS measurements only.< |
| Starting Page | 345 |
| Ending Page | 347 |
| File Size | 268284 |
| Page Count | 3 |
| File Format | |
| ISBN | 0818654708 |
| DOI | 10.1109/CIC.1993.378433 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1993-09-05 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Electrocardiography Myocardium Network synthesis Arteries Artificial neural networks Medical diagnostic imaging Computer networks Heart Cardiac disease |
| Content Type | Text |
| Resource Type | Article |
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