ECG Classification Using Deep CNN Improved by Wavelet Transform Article Swipe
Yunxiang Zhao
,
Jinyong Cheng
,
Ping Zhan
,
Xueping Peng
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2020.09938
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2020.09938
© 2020 Tech Science Press. All rights reserved. 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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2020.09938
- OA Status
- diamond
- Cited By
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- References
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- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3038561791Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/cmc.2020.09938Digital Object Identifier
- Title
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ECG Classification Using Deep CNN Improved by Wavelet TransformWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-01-01Full publication date if available
- Authors
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Yunxiang Zhao, Jinyong Cheng, Ping Zhan, Xueping PengList of authors in order
- Landing page
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https://doi.org/10.32604/cmc.2020.09938Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.32604/cmc.2020.09938Direct OA link when available
- Concepts
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Convolutional neural network, Artificial intelligence, Wavelet transform, Pattern recognition (psychology), Wavelet, Computer science, Deep learning, Discrete wavelet transformTop concepts (fields/topics) attached by OpenAlex
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47Total citation count in OpenAlex
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2025: 9, 2024: 11, 2023: 8, 2022: 11, 2021: 8Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.with | 25, 54, 137 |
| abstract_inverted_index.87.1% | 128 |
| abstract_inverted_index.After | 121 |
| abstract_inverted_index.Since | 37 |
| abstract_inverted_index.after | 68 |
| abstract_inverted_index.based | 20 |
| abstract_inverted_index.cross | 122 |
| abstract_inverted_index.kinds | 51 |
| abstract_inverted_index.paper | 106 |
| abstract_inverted_index.score | 133 |
| abstract_inverted_index.them. | 104 |
| abstract_inverted_index.Atrial | 8 |
| abstract_inverted_index.Press. | 4 |
| abstract_inverted_index.common | 13 |
| abstract_inverted_index.easily | 42 |
| abstract_inverted_index.higher | 146 |
| abstract_inverted_index.method | 19, 109, 125 |
| abstract_inverted_index.neural | 28, 80 |
| abstract_inverted_index.noise. | 76 |
| abstract_inverted_index.obtain | 127 |
| abstract_inverted_index.rights | 6 |
| abstract_inverted_index.scales | 57 |
| abstract_inverted_index.signal | 40, 46, 153 |
| abstract_inverted_index.sizes, | 94 |
| abstract_inverted_index.86.46%. | 135 |
| abstract_inverted_index.Science | 3 |
| abstract_inverted_index.ability | 150 |
| abstract_inverted_index.applied | 31 |
| abstract_inverted_index.applies | 107 |
| abstract_inverted_index.carried | 66 |
| abstract_inverted_index.extract | 85 |
| abstract_inverted_index.finally | 96 |
| abstract_inverted_index.kernels | 91 |
| abstract_inverted_index.method, | 141 |
| abstract_inverted_index.network | 29, 81 |
| abstract_inverted_index.softmax | 98 |
| abstract_inverted_index.wavelet | 22, 59, 63 |
| abstract_inverted_index.24-layer | 78 |
| abstract_inverted_index.Compared | 136 |
| abstract_inverted_index.accuracy | 129, 147 |
| abstract_inverted_index.classify | 103 |
| abstract_inverted_index.combined | 24 |
| abstract_inverted_index.existing | 139 |
| abstract_inverted_index.features | 88 |
| abstract_inverted_index.proposed | 143 |
| abstract_inverted_index.provided | 115 |
| abstract_inverted_index.algorithm | 144 |
| abstract_inverted_index.automatic | 33 |
| abstract_inverted_index.different | 55, 93 |
| abstract_inverted_index.eliminate | 72 |
| abstract_inverted_index.filtering | 70 |
| abstract_inverted_index.frequency | 56 |
| abstract_inverted_index.function, | 60 |
| abstract_inverted_index.inferred, | 43 |
| abstract_inverted_index.influence | 74 |
| abstract_inverted_index.reserved. | 7 |
| abstract_inverted_index.segmented | 69 |
| abstract_inverted_index.transform | 23 |
| abstract_inverted_index.challenge. | 120 |
| abstract_inverted_index.classifier | 99 |
| abstract_inverted_index.decomposed | 48 |
| abstract_inverted_index.persistent | 14 |
| abstract_inverted_index.subsignals | 53 |
| abstract_inverted_index.arrhythmia. | 17 |
| abstract_inverted_index.convolution | 79, 90 |
| abstract_inverted_index.validation, | 123 |
| abstract_inverted_index.fibrillation | 9 |
| abstract_inverted_index.hierarchical | 87 |
| abstract_inverted_index.convolutional | 27 |
| abstract_inverted_index.PhysioNet/CINC | 119 |
| abstract_inverted_index.classification | 34, 140 |
| abstract_inverted_index.generalization | 149 |
| abstract_inverted_index.reconstruction | 64 |
| abstract_inverted_index.classification. | 155 |
| abstract_inverted_index.electrocardiograms. | 36 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.95468685 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |