Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation Article Swipe
Nitish Katal
,
Saurav Gupta
,
Pankaj Verma
,
Bhisham Sharma
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics13243605
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics13243605
Heart diseases is the world’s principal cause of death, and arrhythmia poses a serious risk to the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia early and accurately, which is essential for immediate treatment and intervention. Deep learning approaches have played an important role in automatically identifying complicated patterns from ECG data, which can be further used to identify arrhythmia. In this paper, deep-learning-based methods for arrhythmia identification using ECG signals are thoroughly studied and their performances evaluated on the basis of accuracy, specificity, precision, and F1 score. We propose the development of a small CNN, and its performance is compared against pretrained models like GoogLeNet. The comparative study demonstrates the promising potential of deep-learning-based arrhythmia identification using ECG signals.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics13243605
- https://www.mdpi.com/2075-4418/13/24/3605/pdf?version=1701772264
- OA Status
- gold
- Cited By
- 38
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389336651
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Raw OpenAlex JSON
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https://openalex.org/W4389336651Canonical identifier for this work in OpenAlex
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Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance EvaluationWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-05Full publication date if available
- Authors
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Nitish Katal, Saurav Gupta, Pankaj Verma, Bhisham SharmaList of authors in order
- Landing page
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https://doi.org/10.3390/diagnostics13243605Publisher landing page
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https://www.mdpi.com/2075-4418/13/24/3605/pdf?version=1701772264Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-4418/13/24/3605/pdf?version=1701772264Direct OA link when available
- Concepts
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Deep learning, Cardiac arrhythmia, Artificial intelligence, Computer science, Identification (biology), Machine learning, F1 score, Pattern recognition (psychology), Medicine, Cardiology, Atrial fibrillation, Botany, BiologyTop concepts (fields/topics) attached by OpenAlex
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38Total citation count in OpenAlex
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2025: 22, 2024: 16Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
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| primary_location.pdf_url | https://www.mdpi.com/2075-4418/13/24/3605/pdf?version=1701772264 |
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| primary_location.landing_page_url | https://doi.org/10.3390/diagnostics13243605 |
| publication_date | 2023-12-05 |
| publication_year | 2023 |
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