{"_id":"aZwCGgjPM9QdYmEbu","bibbaseid":"milletroig-venturagaliano-chorrogasco-cebrian-supportvectormachineforarrhythmiadiscriminationwithwavelettransformbasedfeatureselection-2000","downloads":0,"creationDate":"2016-05-08T13:37:37.731Z","title":"Support vector machine for arrhythmia discrimination with wavelet\ntransform-based feature selection","author_short":["Millet-Roig, J.","Ventura-Galiano, R.","Chorro-Gasco, F.","Cebrian, a."],"year":2000,"bibtype":"article","biburl":null,"bibdata":{"title":"Support vector machine for arrhythmia discrimination with wavelet\ntransform-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\nsolving classification or pattern recognition problems. The present\ncontribution is devoted to applying SVM to malignant arrhythmias\ndiscrimination. The Wavelet Transform was applied to single-lead\nepisodes of different rhythms belonging to various patients. More than\n50 characteristic parameters were extracted in order to define each\nrhythm. The number of normalized parameters were reduced by means of\nbackward algorithms developed by the authors. SVM was then applied to\nthe reduced normalized parameter set. SVM surpassed other classification\nschemes, including advanced statistical decision methods. Good-accuracy\nclassifications are achieved with just a few support vectors, with the\nconsequent benefit in computational cost. In conclusion, these positive\nresults evidence the potential of SVM techniques in malignant\narrhythmias 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)","bibtex":"@article{\n title = {Support vector machine for arrhythmia discrimination with wavelet\ntransform-based feature selection},\n type = {article},\n year = {2000},\n identifiers = {[object Object]},\n pages = {407-410},\n id = {733e2b88-5c43-3ee7-a0f2-0f4aab084585},\n created = {2015-04-30T11:13:53.000Z},\n file_attached = {true},\n profile_id = {5e73f9af-1f10-3a70-a285-d3fbb699efe2},\n group_id = {e134fb94-8118-3ebe-b82d-bfbf0a8ae633},\n last_modified = {2015-04-30T11:18:44.000Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n abstract = {Support Vector Machines (SVMs), have meant a great advance in\nsolving classification or pattern recognition problems. The present\ncontribution is devoted to applying SVM to malignant arrhythmias\ndiscrimination. The Wavelet Transform was applied to single-lead\nepisodes of different rhythms belonging to various patients. More than\n50 characteristic parameters were extracted in order to define each\nrhythm. The number of normalized parameters were reduced by means of\nbackward algorithms developed by the authors. SVM was then applied to\nthe reduced normalized parameter set. SVM surpassed other classification\nschemes, including advanced statistical decision methods. Good-accuracy\nclassifications are achieved with just a few support vectors, with the\nconsequent benefit in computational cost. In conclusion, these positive\nresults evidence the potential of SVM techniques in malignant\narrhythmias discrimination},\n bibtype = {article},\n author = {Millet-Roig, J. and Ventura-Galiano, R. and Chorro-Gasco, F.J. and Cebrian, a.},\n journal = {Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163)}\n}","author_short":["Millet-Roig, J.","Ventura-Galiano, R.","Chorro-Gasco, F.","Cebrian, a."],"urls":{"Paper":"http://bibbase.org/service/mendeley/5e73f9af-1f10-3a70-a285-d3fbb699efe2/file/1ced6ab1-b287-d4bf-4025-42411b49ce22/2000-Support_vector_machine_for_arrhythmia_discrimination_with_wavelet_transform-based_feature_selection.pdf.pdf"},"bibbaseid":"milletroig-venturagaliano-chorrogasco-cebrian-supportvectormachineforarrhythmiadiscriminationwithwavelettransformbasedfeatureselection-2000","role":"author","downloads":0},"search_terms":["support","vector","machine","arrhythmia","discrimination","wavelet","transform","based","feature","selection","millet-roig","ventura-galiano","chorro-gasco","cebrian"],"keywords":[],"authorIDs":[]}