ECG Classification Using Deep CNN Improved by Wavelet Transform
Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds...
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| Veröffentlicht in: | Computers, materials & continua materials & continua, 2020-01, Vol.64 (3), p.1615-1628 |
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| creator | Zhao, Yunxiang Cheng, Jinyong Zhan, Ping Peng, Xueping |
| description | Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge. After cross validation, this method can obtain 87.1% accuracy and the F1 score is 86.46%. Compared with the existing classification method, our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification. |
| doi_str_mv | 10.32604/cmc.2020.09938 |
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A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge. After cross validation, this method can obtain 87.1% accuracy and the F1 score is 86.46%. 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Compared with the existing classification method, our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification.</description><subject>Algorithms</subject><subject>Arrhythmia</subject><subject>Artificial neural networks</subject><subject>Classification</subject><subject>Convolution</subject><subject>Electrocardiography</subject><subject>Feature extraction</subject><subject>Fibrillation</subject><subject>Neural networks</subject><subject>Signal classification</subject><subject>Wavelet transforms</subject><issn>1546-2226</issn><issn>1546-2218</issn><issn>1546-2226</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNkDFPwzAUhC0EEqUws1piTvr87Lj2hFAopVJVllaMlpPYKFWTFDut1H9PaBmY7obT3ekj5JFBylGCmJRNmSIgpKA1V1dkxDIhE0SU1__8LbmLcQvAJdcwIs-zfE7znY2x9nVp-7pr6SbW7Rd9dW5P89WKLpp96I6uosWJftqj27meroNto-9Cc09uvN1F9_CnY7J5m63z92T5MV_kL8uk5Az6xDsps6qYAvdCW8-01woF14xzLICVCFapKqvQZQVkhWB2qiQ6y5UX6C3nY_J06R2-fB9c7M22O4R2mDQomEbgAtWQmlxSZehiDM6bfagbG06GgTlTMgMl80vJnCnxH70KWHw</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Zhao, Yunxiang</creator><creator>Cheng, Jinyong</creator><creator>Zhan, Ping</creator><creator>Peng, Xueping</creator><general>Tech Science Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20200101</creationdate><title>ECG Classification Using Deep CNN Improved by Wavelet Transform</title><author>Zhao, Yunxiang ; Cheng, Jinyong ; Zhan, Ping ; Peng, Xueping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c310t-fe665db703f49af19f9824391332b01c20a88d5d2e5b05b41a7862ea38f42fa33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Arrhythmia</topic><topic>Artificial neural networks</topic><topic>Classification</topic><topic>Convolution</topic><topic>Electrocardiography</topic><topic>Feature extraction</topic><topic>Fibrillation</topic><topic>Neural networks</topic><topic>Signal classification</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Yunxiang</creatorcontrib><creatorcontrib>Cheng, Jinyong</creatorcontrib><creatorcontrib>Zhan, Ping</creatorcontrib><creatorcontrib>Peng, Xueping</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Computers, materials & continua</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Yunxiang</au><au>Cheng, Jinyong</au><au>Zhan, Ping</au><au>Peng, Xueping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ECG Classification Using Deep CNN Improved by Wavelet Transform</atitle><jtitle>Computers, materials & continua</jtitle><date>2020-01-01</date><risdate>2020</risdate><volume>64</volume><issue>3</issue><spage>1615</spage><epage>1628</epage><pages>1615-1628</pages><issn>1546-2226</issn><issn>1546-2218</issn><eissn>1546-2226</eissn><abstract>Atrial fibrillation is the most common persistent form of arrhythmia. 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| subjects | Algorithms Arrhythmia Artificial neural networks Classification Convolution Electrocardiography Feature extraction Fibrillation Neural networks Signal classification Wavelet transforms |
| title | ECG Classification Using Deep CNN Improved by Wavelet Transform |
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