Identifying Premature Ventricular Complexes from Outflow Tracts Based on PVC Configuration: A Machine Learning Approach Article Swipe
Sargun Bajaj
,
Matthew T. Bennett
,
Simon W. Rabkin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jcm12175558
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jcm12175558
Background: Current inferences about the site of origin (SOO) of premature ventricular complexes (PVC) from the surface ECG have not been subjected to newer data analytic techniques that identify signals that are not recognized by visual inspection. Aims: The objective of this study was to apply data analytics to PVC characteristics. Methods: PVCs from 12-lead ECGs of a consecutive series of 338 individuals were examined by unsupervised machine learning cluster analysis, and indexes were compared to a composite criterion for SOO. Results: Data analytics found that V1S plus V2S ≤ 9.25 of the PVC had a LVOT origin (sensitivity 95.4%; specificity 97.5%). V1R + V2R + V3R > 15.0 (a RBBB configuration) likely had a LVOT origin. PVCs with V1S plus V2S > 12.75 (LBBB configuration) likely had a RVOT origin. PVC with V1S plus V2S > 14.25 (LBBB configuration) and all inferior leads positive likely had a RVOT origin. Conclusion: Newer data analytic techniques provide a non-invasive approach to identifying PVC SOO, which should be useful for the clinician evaluating a 12-lead ECG.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jcm12175558
- https://www.mdpi.com/2077-0383/12/17/5558/pdf?version=1693272240
- OA Status
- gold
- Cited By
- 6
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386220165
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https://openalex.org/W4386220165Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jcm12175558Digital Object Identifier
- Title
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Identifying Premature Ventricular Complexes from Outflow Tracts Based on PVC Configuration: A Machine Learning ApproachWork title
- Type
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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
-
2023-08-26Full publication date if available
- Authors
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Sargun Bajaj, Matthew T. Bennett, Simon W. RabkinList of authors in order
- Landing page
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https://doi.org/10.3390/jcm12175558Publisher landing page
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https://www.mdpi.com/2077-0383/12/17/5558/pdf?version=1693272240Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2077-0383/12/17/5558/pdf?version=1693272240Direct OA link when available
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Medicine, Cardiology, Internal medicine, Analytics, Cluster (spacecraft), Unsupervised learning, Artificial intelligence, Machine learning, Data mining, Computer science, Programming languageTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 2, 2024: 4Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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