Ectopic Heartbeat Detection from ECG Signals using Deep Convolutional Neural Networks Article Swipe
A Hasitha Kuruwita
,
Ng Shu Kay
,
Alan Wee‐Chung Liew
,
Brent Richards
,
Kelvin Ross
,
Kuldeep Kumar
,
Luke J. Haseler
,
Meghan McConnell
,
Ping Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/bibm55620.2022.9995477
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/bibm55620.2022.9995477
Electrocardiogram (ECG) signal analysis is widely used to diagnose various cardiac and non-cardiac diseases. Detecting abnormalities on ECG is critical for preventing the onset of life-threatening cardiac arrhythmias. This paper proposed a method based on deep convolutional neural network (DCNN) to detect abnormal heartbeats such as ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). The proposed model was trained and validated on two large-sample PhysioNet’s MIT-BIH datasets. A separate test result showed overall accuracy of 96% on distinguishing three types of heartbeats VEB, SVEB, and other heartbeats which are not ectopic beat (NOTEB).
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Metadata
- Type
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- Language
- en
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- https://doi.org/10.1109/bibm55620.2022.9995477
- OA Status
- green
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- 2
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1109/bibm55620.2022.9995477Digital Object Identifier
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Ectopic Heartbeat Detection from ECG Signals using Deep Convolutional Neural NetworksWork title
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articleOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-12-06Full publication date if available
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A Hasitha Kuruwita, Ng Shu Kay, Alan Wee‐Chung Liew, Brent Richards, Kelvin Ross, Kuldeep Kumar, Luke J. Haseler, Meghan McConnell, Ping ZhangList of authors in order
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https://doi.org/10.1109/bibm55620.2022.9995477Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://hdl.handle.net/10072/421192Direct OA link when available
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Heartbeat, Computer science, Convolutional neural network, Artificial intelligence, Speech recognition, Pattern recognition (psychology), Deep learning, Computer securityTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2997681386, https://openalex.org/W4221132540, https://openalex.org/W3127657277, https://openalex.org/W2073358698, https://openalex.org/W1973007593, https://openalex.org/W3112135657, https://openalex.org/W2169776329, https://openalex.org/W2158600543, https://openalex.org/W3165453231, https://openalex.org/W2091474465, https://openalex.org/W2809254203, https://openalex.org/W2144354855, https://openalex.org/W2811482466, https://openalex.org/W2087461551, https://openalex.org/W3120332919, https://openalex.org/W2289846183, https://openalex.org/W2969771517, https://openalex.org/W2938651781, https://openalex.org/W2140920882, https://openalex.org/W3158600136, https://openalex.org/W2114842946, https://openalex.org/W3015001108, https://openalex.org/W2077887234, https://openalex.org/W2907292205, https://openalex.org/W2017263726, https://openalex.org/W2223222085, https://openalex.org/W3100777112, https://openalex.org/W2085006623, https://openalex.org/W2966623823, https://openalex.org/W2302255633, https://openalex.org/W2162800060, https://openalex.org/W2792396409, https://openalex.org/W2751547580 |
| referenced_works_count | 33 |
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| abstract_inverted_index.(NOTEB). | 93 |
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| countries_distinct_count | 1 |
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.62774958 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |