Classification of Electrocardiogram Signals Based on Hybrid Deep Learning Models Article Swipe
Surbhi Bhatia
,
Saroj Kumar Pandey
,
Ankit Kumar
,
Asma Alshuhail
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su142416572
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su142416572
According to the analysis of the World Health Organization (WHO), the diagnosis and treatment of heart diseases is the most difficult task. Several algorithms for the classification of arrhythmic heartbeats from electrocardiogram (ECG) signals have been developed over the past few decades, using computer-aided diagnosis systems. Deep learning architecture adaption is a recent effective advancement of deep learning techniques in the field of artificial intelligence. In this study, we developed a new deep convolutional neural network (CNN) and bidirectional long-term short-term memory network (BLSTM) model to automatically classify ECG heartbeats into five different groups based on the ANSI-AAMI standard. End-to-end learning (feature extraction and classification work together) is done in this hybrid model without extracting manual features. The experiment is performed on the publicly accessible PhysioNet MIT-BIH arrhythmia database, and the findings are compared with results from the other two hybrid deep learning models, which are a combination of CNN and LSTM and CNN and Gated Recurrent Unit (GRU). The performance of the model is also compared with existing works cited in the literature. Using the SMOTE approach, this database was artificially oversampled to address the class imbalance problem. This new hybrid model was trained on the oversampled ECG database and validated using tenfold cross-validation on the actual test dataset. According to experimental observations, the developed hybrid model outperforms in terms of recall, precision, accuracy and F-score performance of the hybrid model are 94.36%, 89.4%, 98.36% and 91.67%, respectively, which is better than the existing methods.
Related Topics
Concepts
Computer science
Artificial intelligence
Deep learning
Convolutional neural network
Feature extraction
Field (mathematics)
Machine learning
Artificial neural network
Task (project management)
Recall
Pattern recognition (psychology)
Feature (linguistics)
Pure mathematics
Philosophy
Linguistics
Economics
Mathematics
Management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su142416572
- https://www.mdpi.com/2071-1050/14/24/16572/pdf?version=1670919971
- OA Status
- gold
- Cited By
- 23
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312125461
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312125461Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su142416572Digital Object Identifier
- Title
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Classification of Electrocardiogram Signals Based on Hybrid Deep Learning ModelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-10Full publication date if available
- Authors
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Surbhi Bhatia, Saroj Kumar Pandey, Ankit Kumar, Asma AlshuhailList of authors in order
- Landing page
-
https://doi.org/10.3390/su142416572Publisher landing page
- PDF URL
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https://www.mdpi.com/2071-1050/14/24/16572/pdf?version=1670919971Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2071-1050/14/24/16572/pdf?version=1670919971Direct OA link when available
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Computer science, Artificial intelligence, Deep learning, Convolutional neural network, Feature extraction, Field (mathematics), Machine learning, Artificial neural network, Task (project management), Recall, Pattern recognition (psychology), Feature (linguistics), Pure mathematics, Philosophy, Linguistics, Economics, Mathematics, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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23Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 14, 2023: 4Per-year citation counts (last 5 years)
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54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ECG | 88, 198 |
| abstract_inverted_index.The | 117, 159 |
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| abstract_inverted_index.which | 144, 239 |
| abstract_inverted_index.works | 169 |
| abstract_inverted_index.(GRU). | 158 |
| abstract_inverted_index.(WHO), | 9 |
| abstract_inverted_index.89.4%, | 234 |
| abstract_inverted_index.98.36% | 235 |
| abstract_inverted_index.Health | 7 |
| abstract_inverted_index.actual | 207 |
| abstract_inverted_index.better | 241 |
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| abstract_inverted_index.study, | 67 |
| abstract_inverted_index.(BLSTM) | 83 |
| abstract_inverted_index.91.67%, | 237 |
| abstract_inverted_index.94.36%, | 233 |
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| abstract_inverted_index.Recurrent | 156 |
| abstract_inverted_index.approach, | 177 |
| abstract_inverted_index.database, | 128 |
| abstract_inverted_index.developed | 36, 69, 215 |
| abstract_inverted_index.diagnosis | 11, 44 |
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| abstract_inverted_index.extraction | 102 |
| abstract_inverted_index.heartbeats | 29, 89 |
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