A novel machine learning framework for automated detection of arrhythmias in ECG segments-Reference-Cited by-同舟云学术

A novel machine learning framework for automated detection of arrhythmias in ECG segments

Published:2021-01-23 Issue:11 Volume:12 Page:10145-10162
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Pham The-Hanh^ORCID,Sree Vinitha,Mapes John,Dua Sumeet,Lih Oh Shu,Koh Joel E. W.,Ciaccio Edward J.,Acharya U. Rajendra

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s12652-020-02779-1.pdf

Reference34 articles.

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3. Acharya UR, Fujita H, Adam M, Oh SL, Tan JH, Sudarshan VK, Koh JEW (2016) Automated characterization of arrhythmias using nonlinear features from tachycardia ECG beats. In: IEEE international conference on systems, man, and cybernetics, 2016.

4. Alfaouri M, Daqrouq K (2008) ECG signal denoising by wavelet transform thresholding. Am J Appl Sci 5:276–281. https://doi.org/10.3844/ajassp.2008.276.281

5. Ashtiyani M, Navaei Lavasani S, Asgharzadeh Alvar A, Deevband MR (2018) Heart rate variability classification using support vector machine and genetic algorithm. J Biomed Phys Eng. 8:423–434

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