Detection of premature ventricular contraction (PVC) using linear and nonlinear techniques: an experimental study
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Link
http://link.springer.com/content/pdf/10.1007/s10586-019-02953-x.pdf
Reference44 articles.
1. Asl, B.M., Setarehdan, S.K., Mohebbi, M.: Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif. Intell. Med. 44(1), 51–64 (2008)
2. Ge, D., Srinivasan, N., Krishnan, S.M.: Cardiac arrhythmia classification using autoregressive modeling. Biomed. Eng. Online 1(1), 5 (2002)
3. Acharya, U.R., Joseph, K.P., Kannathal, N., Lim, C.M., Suri, J.S.: Heart rate variability: a review. Med. Biol. Eng. Comput. 44(12), 1031–1051 (2006)
4. Berkaya, S.K., Uysal, A.K., Gunal, E.S., Ergin, S., Gunal, S., Gulmezoglu, M.B.: A survey on ECG analysis. Biomed. Signal Process. Control 43, 216–235 (2018)
5. Chen, Z., Xu, H., Luo, J., Zhu, T., Meng, J.: Low-power perceptron model based ECG processor for premature ventricular contraction detection. Microprocess. Microsyst. 59, 29–36 (2018)
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