Arrhythmia Classification Using Nearest Neighbor Search with Principal Component Analysis
- Title
- Arrhythmia Classification Using Nearest Neighbor Search with Principal Component Analysis
- Author
- 강경태
- Keywords
- Arrythmia classification; Principal component analysis; κ-nearest neighbour
- Issue Date
- 2015-09
- Publisher
- Association for Computing Machinery
- Citation
- BCB '15 Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, Page. 553-555
- Abstract
- Arrhythmia is currently classified by rate, mechanism, or duration, and many experts are using different techniques to classify arrhythmia. The present group of researchers have developed an automated method to select useful heartbeat features, which were then applied to a κ-nearest neighbor algorithm of arrhythmia classification. The arrhythmia dataset from the University of California, Irvine, Machine Learning Repository was applied to test the performance of our method, yielding a classification accuracy of 98%. Copyright is held by the author/owner(s).
- URI
- https://dl.acm.org/citation.cfm?doid=2808719.2811573https://repository.hanyang.ac.kr/handle/20.500.11754/101520
- ISBN
- 978-1-4503-3853-0
- DOI
- 10.1145/2808719.2811573
- Appears in Collections:
- COLLEGE OF COMPUTING[E](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML