Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks-Reference-Cited by-同舟云学术

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Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks

Published:2020-05-19 Issue: Volume: Page:
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Huang Jing-Shan,Chen Bin-Qiang,Zeng Nian-Yin,Cao Xin-Cheng,Li Yang

Funder

National Natural Science Foundation of China

Fundamental Research Funds for Central Universities of the Central South University

International Science and Technology Cooperation Project of Fujian Province of China

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s12652-020-02110-y.pdf

Reference87 articles.

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2. Alickovic E, Subasi A (2015) Effect of multiscale PCA de-noising in ECG beat classification for diagnosis of cardiovascular diseases. Circuits Syst Signal Process 34(2):513–533

3. Alves DK, Costa FB, Ribeiro RL et al (2016) Real-time power measurement using the maximal overlap discrete wavelet-packet transform. IEEE Trans Ind Electron 64(4):3177–3187

4. Andersen RS, Peimankar A, Puthusserypady S (2019) A deep learning approach for real-time detection of atrial fibrillation. Expert Syst Appl 115:465–473

5. Banerjee S, Mitra M (2010) ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform. In: International conference on systems in medicine and biology. IEEE, pp 55–60

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