Abstract
The TELAVIV system for computerised arrhythmia detection and identification in ambulatory ECGs has been extended to include a reliable and rapid computerised method for ST-segment analysis. It now incorporates self-learning analysis and classification of ST-segment changes. Some advantages for ST-segment analysis present in this algorithm are: (a) it analyses three channels simultaneously instead of one or two in other systems; (b) it includes a larger number of parameters describing the ST-segment changes; (c) the measurements are detected from a noise-free template, averaged over 20 beats; (d) isoelectric and J-points are detected by a self-learning process; (e) a scoring system has been added; and (f) it now has the capability to detect and classify arrhythmias and ST-segment changes simultaneously. The system was tested on three types of recordings: 30 1 h recordings in which ST-changes were observed in the quick summary plot produced by the TELAVIV system; 12 recordings taken during submaximal effort; and 13 recordings made during conventional ergometry. The results indicated a high level of sensitivity and specificity of the program.
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Akselrod, S., Norymberg, M., Peled, I. et al. Computerised analysis of ST segment changes in ambulatory electrocardiograms. Med. Biol. Eng. Comput. 25, 513–519 (1987). https://doi.org/10.1007/BF02441743
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DOI: https://doi.org/10.1007/BF02441743