Nearest neighbor search with locally weighted linear regression for heartbeat classification
- Authors
- Park, Juyoung; Bhuiyan, Md Zakirul Alam; Kang, Mingon; Son, Junggab; Kang, Kyungtae
- Issue Date
- Feb-2018
- Publisher
- SPRINGER
- Keywords
- Heartbeat classification; Electrocardiogram monitoring; Locally weighted linear regression; Nearest neighbor search
- Citation
- SOFT COMPUTING, v.22, no.4, pp.1225 - 1236
- Indexed
- SCIE
SCOPUS
- Journal Title
- SOFT COMPUTING
- Volume
- 22
- Number
- 4
- Start Page
- 1225
- End Page
- 1236
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/6801
- DOI
- 10.1007/s00500-016-2410-9
- ISSN
- 1432-7643
- 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.
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