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Nearest neighbor search with locally weighted linear regression for heartbeat classification

Title
Nearest neighbor search with locally weighted linear regression for heartbeat classification
Author
강경태
Keywords
Heartbeat classification; Electrocardiogram monitoring; Locally weighted linear regression; Nearest neighbor search
Issue Date
2018-02
Publisher
SPRINGER
Citation
SOFT COMPUTING, v. 22, No. 4, Page. 1225-1236
Abstract
Automatic interpretation of electrocardiograms provides a noninvasive and inexpensive technique for analyzing the heart activity of patients with a range of cardiac conditions. We propose a method that combines locally weighted linear regression with nearest neighbor search for heartbeat detection and classification in the management of non-life-threatening arrhythmia. In the proposed method, heartbeats are detected and their features are found using the Pan-Tompkins algorithm; then, they are classified by locally weighted linear regression on their nearest neighbors in a training set. The results of evaluation on data from the MIT-BIH arrhythmia database indicate that the proposed method has a sensitivity of 93.68 %, a positive predictive value of 96.62 %, and an accuracy of 98.07 % for type-oriented evaluation; and a sensitivity of 74.15 %, a positive predictive value of 72.5 %, and an accuracy of 88.69 % for patient-oriented evaluation. These results are comparable to those from existing search schemes and contribute to the systematic design of automatic heartbeat classification systems for clinical decision support.
URI
https://link.springer.com/article/10.1007%2Fs00500-016-2410-9https://repository.hanyang.ac.kr/handle/20.500.11754/80935
ISSN
1432-7643
DOI
10.1007/s00500-016-2410-9
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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