ECG Signal Ｒecognition Based on Deep Stacked Network
The traditional electrocardiogram ( ECG) signal recognition algorithms rely on ECG experts to participate in feature recognition，which is time-consuming and laborious with high diagnostic cost. Complex and diverse ECG signal patterns result in low recognition accuracy and poor adaptability. To sol...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2021-06-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1974 |
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| author | ZHANG Ｒiu WANG Ｒu HUANG Jun ZENG Xin |
| author_facet | ZHANG Ｒiu WANG Ｒu HUANG Jun ZENG Xin |
| author_sort | ZHANG Ｒiu |
| collection | DOAJ |
| description | The traditional electrocardiogram ( ECG) signal recognition algorithms rely on ECG experts to
participate in feature recognition，which is time-consuming and laborious with high diagnostic cost. Complex and
diverse ECG signal patterns result in low recognition accuracy and poor adaptability. To solve the above problems，
the stack Sparse Autoencoder was combined with the Softmax classifier to form a Deep stack Network to realize
automatic recognition of ECG signals. The feature extraction of ECG signals was completed by stacking three sparse
autoencoders，and the high-dimensional features of ECG signals were depicted layer by layer，and the ECG signals
were identified by Softmax classifier. Detailed assessment of the model characteristic of Deep stacked Network，
determine the super parameter of the network model，sample training set and test set samples from MIT /BIH
database. The experimental results show that the total recognition rate of the proposed method is 99. 69%，which
verifies the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-df81a898bacd4fdc995af6f1bdf454c7 |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2021-06-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-df81a898bacd4fdc995af6f1bdf454c72025-08-20T02:56:32ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832021-06-01260310811410.15938/j.jhust.2021.03.016ECG Signal Ｒecognition Based on Deep Stacked NetworkZHANG Ｒiu0WANG Ｒu1HUANG Jun2ZENG Xin3School of Automation，Harbin University of Science and Technology，Harbin 150080，ChinaSchool of Automation，Harbin University of Science and Technology，Harbin 150080，ChinaChengdu East Ｒoad Traffic Technology Co. ，Ltd，Chengdu 610037，ChinaSchool of Automation，Harbin University of Science and Technology，Harbin 150080，ChinaThe traditional electrocardiogram ( ECG) signal recognition algorithms rely on ECG experts to participate in feature recognition，which is time-consuming and laborious with high diagnostic cost. Complex and diverse ECG signal patterns result in low recognition accuracy and poor adaptability. To solve the above problems， the stack Sparse Autoencoder was combined with the Softmax classifier to form a Deep stack Network to realize automatic recognition of ECG signals. The feature extraction of ECG signals was completed by stacking three sparse autoencoders，and the high-dimensional features of ECG signals were depicted layer by layer，and the ECG signals were identified by Softmax classifier. Detailed assessment of the model characteristic of Deep stacked Network， determine the super parameter of the network model，sample training set and test set samples from MIT /BIH database. The experimental results show that the total recognition rate of the proposed method is 99. 69%，which verifies the effectiveness of the proposed method.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1974stacked sparse auto-encoderfeature extractionecg signal recognitionsparse parameter |
| spellingShingle | ZHANG Ｒiu WANG Ｒu HUANG Jun ZENG Xin ECG Signal Ｒecognition Based on Deep Stacked Network Journal of Harbin University of Science and Technology stacked sparse auto-encoder feature extraction ecg signal recognition sparse parameter |
| title | ECG Signal Ｒecognition Based on Deep Stacked Network |
| title_full | ECG Signal Ｒecognition Based on Deep Stacked Network |
| title_fullStr | ECG Signal Ｒecognition Based on Deep Stacked Network |
| title_full_unstemmed | ECG Signal Ｒecognition Based on Deep Stacked Network |
| title_short | ECG Signal Ｒecognition Based on Deep Stacked Network |
| title_sort | ecg signal recognition based on deep stacked network |
| topic | stacked sparse auto-encoder feature extraction ecg signal recognition sparse parameter |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1974 |
| work_keys_str_mv | AT zhangriu ecgsignalrecognitionbasedondeepstackednetwork AT wangru ecgsignalrecognitionbasedondeepstackednetwork AT huangjun ecgsignalrecognitionbasedondeepstackednetwork AT zengxin ecgsignalrecognitionbasedondeepstackednetwork |