A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression-Reference-Cited by-同舟云学术

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A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression

Published:2017-04-08 Issue:10 Volume:9 Page:1733-1740
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Yang Jianli,Bai Yang,Lin Feng,Liu Ming,Hou Zengguang,Liu Xiuling

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/article/10.1007/s13042-017-0677-5/fulltext.html

Reference29 articles.

1. Afkhami RG, Azarnia G, Tinati MA (2016) Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals. Pattern Recognit Lett 70:45–51

2. Acir N (2005) Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm. Neural Comput Appl 14(4):299–309

3. Bengio Y (2009) Learning deep architectures for AI. Foundations and trends. Mach Learning 2(1):1–127

4. Braccini G, Edenbrandt L, Lagerholm M, Peterson C, Rauer O, Rittner R, Sornmo L (1997) Self-organizing maps and Hermite functions for classification of ECG complexes. Comp Cardiol. doi: 10.1109/CIC.1997.647924

5. Chazal P, Reilly RB (2003) Automatic classification of ECG beats using waveform shape and heart beat interval features. Proceedings of the IEEE International Conference on Acoustic, Speech and Signal (ICASSP), Hong Kong, China

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