NDLI: Research on Identification Algorithm Based on ECG Signal and Improved Convolutional Neural Network

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Research on Identification Algorithm Based on ECG Signal and Improved Convolutional Neural Network

Content Provider	Scilit
Author	Wan, Dechao Zhang, Luqiao Bai, Yunxiang Xie, Yu
Copyright Year	2021
Description	Journal: Journal of Physics: Conference Series In the research of ECG signal identity recognition, most of them adopt the method of feature extraction and recognition model separation, extract the time domain features, transform domain features of the original signal, or combine the features with the cross domain. Then the model is used to complete the recognition and classification. In this paper, an advanced improved convolutional neural network model is proposed, which integrates feature extraction and classification to complete identity recognition. ECG data selected from the ecg-id database and MIT-BIH arrhythmia database are directly sent to the model for automatic sign extraction after hierarchical denoising with wavelet tools and then identified. This method achieves the highest recognition rate of 98.49% on ecg-id database and 99.35% on ECG data of MIT-BIH arrhythmia database. The high reliability of the algorithm and the universality of wireless sensors in mobile devices make this research has high commercial value.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1757/1/012046/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1757/1/012046
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	1757
Language	English
Publisher	IOP Publishing
Publisher Date	2021-01-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Industrial Engineering
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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