A New Multichannel Parallel Network Framework for the Special Structure of Multilead ECG Article Swipe
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· 2020
· Open Access
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· DOI: https://doi.org/10.1155/2020/8889483
Electrocardiogram (ECG) contains the rhythmic features of continuous heartbeat and morphological features of ECG waveforms and varies among different diseases. Based on ECG signal features, we propose a combination of multiple neural networks, the multichannel parallel neural network (MLCNN-BiLSTM), to explore feature information contained in ECG. The MLCNN channel is used in extracting the morphological features of ECG waveforms. Compared with traditional convolutional neural network (CNN), the MLCNN can accurately extract strong relevant information on multilead ECG while ignoring irrelevant information. It is suitable for the special structures of multilead ECG. The Bidirectional Long Short-Term Memory (BiLSTM) channel is used in extracting the rhythmic features of ECG continuous heartbeat. Finally, by initializing the core threshold parameters and using the backpropagation algorithm to update automatically, the weighted fusion of the temporal-spatial features extracted from multiple channels in parallel is used in exploring the sensitivity of different cardiovascular diseases to morphological and rhythmic features. Experimental results show that the accuracy rate of multiple cardiovascular diseases is 87.81%, sensitivity is 86.00%, and specificity is 87.76%. We proposed the MLCNN-BiLSTM neural network that can be used as the first-round screening tool for clinical diagnosis of ECG.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1155/2020/8889483
- https://downloads.hindawi.com/journals/jhe/2020/8889483.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3110244112
Raw OpenAlex JSON
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https://openalex.org/W3110244112Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2020/8889483Digital Object Identifier
- Title
-
A New Multichannel Parallel Network Framework for the Special Structure of Multilead ECGWork 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
-
2020-12-03Full publication date if available
- Authors
-
Peng Lu, Hao Xi, Bing Zhou, Hongpo Zhang, Yusong Lin, Li-Wei Chen, Yang Gao, Yabin Zhang, Yanhua HuList of authors in order
- Landing page
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https://doi.org/10.1155/2020/8889483Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/jhe/2020/8889483.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/jhe/2020/8889483.pdfDirect OA link when available
- Concepts
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Heartbeat, Computer science, Artificial intelligence, Pattern recognition (psychology), Initialization, Convolutional neural network, Artificial neural network, Feature (linguistics), Backpropagation, Sensitivity (control systems), Waveform, Channel (broadcasting), Telecommunications, Radar, Computer security, Linguistics, Engineering, Electronic engineering, Programming language, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 1, 2024: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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