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Feature Selection for Interpatient Supervised Heart Beat Classification
Joint Authors
Doquire, G.
François, D.
de Lannoy, G.
Verleysen, M.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-07-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Supervised and interpatient classification of heart beats is primordial in many applications requiring long-term monitoring of the cardiac function.
Several classification models able to cope with the strong class unbalance and a large variety of feature sets have been proposed for this task.
In practice, over 200 features are often considered, and the features retained in the final model are either chosen using domain knowledge or an exhaustive search in the feature sets without evaluating the relevance of each individual feature included in the classifier.
As a consequence, the results obtained by these models can be suboptimal and difficult to interpret.
In this work, feature selection techniques are considered to extract optimal feature subsets for state-of-the-art ECG classification models.
The performances are evaluated on real ambulatory recordings and compared to previously reported feature choices using the same models.
Results indicate that a small number of individual features actually serve the classification and that better performances can be achieved by removing useless features.
American Psychological Association (APA)
Doquire, G.& de Lannoy, G.& François, D.& Verleysen, M.. 2011. Feature Selection for Interpatient Supervised Heart Beat Classification. Computational Intelligence and Neuroscience،Vol. 2011, no. 2011, pp.1-9.
https://search.emarefa.net/detail/BIM-487715
Modern Language Association (MLA)
Doquire, G.…[et al.]. Feature Selection for Interpatient Supervised Heart Beat Classification. Computational Intelligence and Neuroscience No. 2011 (2011), pp.1-9.
https://search.emarefa.net/detail/BIM-487715
American Medical Association (AMA)
Doquire, G.& de Lannoy, G.& François, D.& Verleysen, M.. Feature Selection for Interpatient Supervised Heart Beat Classification. Computational Intelligence and Neuroscience. 2011. Vol. 2011, no. 2011, pp.1-9.
https://search.emarefa.net/detail/BIM-487715
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-487715