@article{
 title = {ECG classification with neural networks and cluster analysis},
 type = {article},
 year = {1991},
 identifiers = {[object Object]},
 pages = {177-180},
 id = {f0ae8ff4-4c8d-34e4-bdf1-dee242f9cd1b},
 created = {2015-04-30T11:13:52.000Z},
 file_attached = {true},
 profile_id = {5e73f9af-1f10-3a70-a285-d3fbb699efe2},
 group_id = {e134fb94-8118-3ebe-b82d-bfbf0a8ae633},
 last_modified = {2015-04-30T11:18:29.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {The combination of two techniques of pattern recognition i.e.,
cluster analysis and neural networks, is investigated in the specific
problem of the diagnostic classification of 12-lead electrocardiograms
(ECGs). For this study a previously used database, established at the
University of Leuven, has been employed. Sensitivity, specificity, and
total and partial accuracy were the indices used for the assessment of
the performance. Several neural networks have been obtained by either
varying the training set (considering clusters of the original learning
set) or adjusting some components of the architecture of the networks.
The combination of different neural networks has shown satisfactory
performances in the diagnostic classification task},
 bibtype = {article},
 author = {Bortolan, G. and Degani, R. and Willems, J.L.},
 journal = {[1991] Proceedings Computers in Cardiology}
}

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