@article{
 title = {Support vector machine for arrhythmia discrimination with wavelet
transform-based feature selection},
 type = {article},
 year = {2000},
 identifiers = {[object Object]},
 pages = {407-410},
 id = {733e2b88-5c43-3ee7-a0f2-0f4aab084585},
 created = {2015-04-30T11:13:53.000Z},
 file_attached = {true},
 profile_id = {5e73f9af-1f10-3a70-a285-d3fbb699efe2},
 group_id = {e134fb94-8118-3ebe-b82d-bfbf0a8ae633},
 last_modified = {2015-04-30T11:18:44.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {Support Vector Machines (SVMs), have meant a great advance in
solving classification or pattern recognition problems. The present
contribution is devoted to applying SVM to malignant arrhythmias
discrimination. The Wavelet Transform was applied to single-lead
episodes of different rhythms belonging to various patients. More than
50 characteristic parameters were extracted in order to define each
rhythm. The number of normalized parameters were reduced by means of
backward algorithms developed by the authors. SVM was then applied to
the reduced normalized parameter set. SVM surpassed other classification
schemes, including advanced statistical decision methods. Good-accuracy
classifications are achieved with just a few support vectors, with the
consequent benefit in computational cost. In conclusion, these positive
results evidence the potential of SVM techniques in malignant
arrhythmias discrimination},
 bibtype = {article},
 author = {Millet-Roig, J. and Ventura-Galiano, R. and Chorro-Gasco, F.J. and Cebrian, a.},
 journal = {Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163)}
}

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