Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System.
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4604143?pdf=render |
| _version_ | 1819059779350298624 |
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| author | Vessela Krasteva Irena Jekova Remo Leber Ramun Schmid Roger Abächerli |
| author_facet | Vessela Krasteva Irena Jekova Remo Leber Ramun Schmid Roger Abächerli |
| author_sort | Vessela Krasteva |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T14:16:31Z |
| format | Article |
| id | doaj.art-282dd09b1a244e4289a894f3292f66b0 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T14:16:31Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-282dd09b1a244e4289a894f3292f66b02022-12-21T19:00:53ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-011010e014012310.1371/journal.pone.0140123Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System.Vessela KrastevaIrena JekovaRemo LeberRamun SchmidRoger AbächerliThis 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.http://europepmc.org/articles/PMC4604143?pdf=render |
| spellingShingle | Vessela Krasteva Irena Jekova Remo Leber Ramun Schmid Roger Abächerli Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System. PLoS ONE |
| title | Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System. |
| title_full | Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System. |
| title_fullStr | Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System. |
| title_full_unstemmed | Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System. |
| title_short | Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System. |
| title_sort | superiority of classification tree versus cluster fuzzy and discriminant models in a heartbeat classification system |
| url | http://europepmc.org/articles/PMC4604143?pdf=render |
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