ECG Delineation with Randomly Selected Wavelet Feature and Random Forest Classifier-Reference-Cited by-同舟云学术

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ECG Delineation with Randomly Selected Wavelet Feature and Random Forest Classifier

Published:2018-08-01 Issue:8 Volume:E101.D Page:2082-2091
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

FU Dapeng¹,XIA Zhourui²,GAO Pengfei³,WANG Haiqing⁴,LIN Jianping⁵,SUN Li⁵

Affiliation:

1. Beijing Zhong Guan Cun Hospital, Chinese Academy of Sciences Zhong Guan Cun Hospital

2. Department of Electronic Engineering, Beijing University of Posts and Telecommunications

3. Department of Electronic Engineering, Tsinghua University

4. Research and Education Dept., Beijing Zhong Guan Cun Hospital, Chinese Academy of Sciences Zhong Guan Cun Hospital

5. Beijing XinHeYiDian Technology Co. Ltd.

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E101.D/8/E101.D_2017EDP7410/_pdf

Reference25 articles.

1. [1] A. Gacek and W. Pedrycz, ECG signal processing, classification and interpretation: a comprehensive framework of computational intelligence, Springer Science & Business Media, 2011. 10.1007/978-0-85729-868-3_3

2. [2] B.U. Kohler, C. Hennig, and R. Orglmeister, “The principles of software QRS detection,” IEEE Eng. Med. Biol. Mag., vol.21, no.1, pp.42-57, 2002. 10.1109/51.993193

3. [3] S. Pal and M. Mitra, “Empirical mode decomposition based ECG enhancement and QRS detection,” Computers in biology and medicine, vol.42, no.1, pp.83-92, 2012. 10.1016/j.compbiomed.2011.10.012

4. [4] A.G. Ramakrishnan, A.P. Prathosh, and T.V. Ananthapadmanabha, “Threshold-independent QRS detection using the dynamic plosion index,” IEEE Signal Process. Lett., vol.21, no.5, pp.554-558, 2014. 10.1109/lsp.2014.2308591

5. [5] P. Laguna, N.V. Thakor, P. Caminal, R. Jané, H.R. Yoon, A. Bayés de Luna, V. Marti, and J. Guindo, “New algorithm for QT interval analysis in 24-hour holter ECG: performance and applications,” Medical and Biological Engineering and Computing, vol.28, no.1, pp.67-73, 1990. 10.1007/bf02441680

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2. Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm;BMC Medical Informatics and Decision Making;2023-07-28

3. Adaptive Region Aggregation Network: Unsupervised Domain Adaptation with Adversarial Training for ECG Delineation;ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2020-05

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