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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Acknowledgments
This work was partly supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8501-16-1018)) supervised by the IITP (Institute for Information & communications Technology Promotion), and partly supported by IITP grant funded by the Korea government (MSIP) (No. B0101-15-0557, Resilient Cyber-Physical Systems Research).
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Park, J., Bhuiyan, M.Z.A., Kang, M. et al. Nearest neighbor search with locally weighted linear regression for heartbeat classification. Soft Comput 22, 1225–1236 (2018). https://doi.org/10.1007/s00500-016-2410-9
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DOI: https://doi.org/10.1007/s00500-016-2410-9