Classification of electrocardiogram signals with waveform morphological analysis and support vector machines-Reference-Cited by-同舟云学术

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Classification of electrocardiogram signals with waveform morphological analysis and support vector machines

Published:2021-10-30 Issue:1 Volume:60 Page:109-119
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Li Hongqiang^ORCID,An Zhixuan,Zuo Shasha,Zhu Wei,Cao Lu,Mu Yuxin,Song Wenchao,Mao Quanhua,Zhang Zhen,Li Enbang,García Juan Daniel Prades

Funder

the Tianjin Key Research and Development Program

the Tianjin Major Project for Civil-Military Integration of Science and Technology

the Tianjin Science and Technology Program

the Tianjin Municipal Special Foundation for Key Cultivation of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-021-02461-4.pdf

Reference24 articles.

1. Benjamin EJ, Muntner P, Alonso A et al (2019) Heart disease and stroke statistics-2019 update: A report from the American Heart Association. Circulation 139:56–66

2. Angelantonio ED, Kaptoge S, Pennells L et al (2019) World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Global Health 7:1332–1345

3. Herry CL, Frasch M, Seely AJE, Wu HT (2017) Heart beat classification from single-lead ECG using the synchrosqueezing transform. Physiol Meas 38:171–187

4. Lenis G, Pilia N, Oesterlein T et al (2016) P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference. Biomed Eng-Biomed Te 61:37–56

5. Karimipour A, Homaeinezhad MR (2014) Real-time electrocardiogram P-QRS-T detection-delineation algorithm based on quality-supported analysis of characteristic templates. Comput Biol Med 52:153–165

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