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10.1371/journal.pone.0140123 http://scihub22266oqcxt.onion/10.1371/journal.pone.0140123 C4604143!4604143!26461492     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26461492&cmd=llinks): Failed to open stream: HTTP request failed! 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Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System |pdf=|usr=}}{{26461492}} gab.com Text Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26461492 #FOAvip This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular (VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference) beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA) and classification tree (CT), all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features), Fuzzy (72 features), LDA (142 coefficients), CT (221 decision nodes) with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%), LDA (99.6%), Cluster (99.5%), Fuzzy (99.4%); sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies): CT (96.7%), Fuzzy (94.4%), LDA (94.2%), Cluster (92.4%); positive predictivity: CT (99.2%), Cluster (93.6%), LDA (93.0%), Fuzzy (92.4%). CT has superior accuracy by 0.3?6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable ?if-then? rules. Twit Text FOAVip Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26461492 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26461492 #FOAvip English Wikipedia <ref>{{Cite pmid|26461492}}</ref> Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System #MMPMID26461492 Krasteva V; Jekova I; Leber R; Schmid R; Ab�cherli R PLoS One 2015[]; 10 (10): � PMID26461492show ga This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular (VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference) beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA) and classification tree (CT), all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features), Fuzzy (72 features), LDA (142 coefficients), CT (221 decision nodes) with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%), LDA (99.6%), Cluster (99.5%), Fuzzy (99.4%); sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies): CT (96.7%), Fuzzy (94.4%), LDA (94.2%), Cluster (92.4%); positive predictivity: CT (99.2%), Cluster (93.6%), LDA (93.0%), Fuzzy (92.4%). CT has superior accuracy by 0.3?6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable ?if-then? rules. �Superiority of Classification Tree versus Cluster, Fuzzy and Discrimin(epmc).pdf DeepDyve Pubget Overpricing