Influence of the Training Set Composition on the Estimation Performance of Linear ECG-Lead Transformations Article Swipe
Daniel Guldenring
,
Dewar Finlay
,
Raymond Bond
,
Alan Alan Kennedy
,
Peter Doggart
,
Ghalib Janjua
,
James McLaughlin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22489/cinc.2023.263
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22489/cinc.2023.263
Linear ECG-lead transformations (LELTs) are used to estimate unrecorded target leads by applying a number of recorded basis leads to a LELT matrix. Such LELT matrices are commonly developed using training datasets that are composed of ECGs that belong to different diagnostic classes (DCs). The aim of our research was to assess the influence of the training set composition on the estimation performance of LELTs that estimate target leads V1, V3, V4 and V6 from basis leads I, II, V2 and V5 of the 12-lead ECG. Our assessment was performed using ECGs from the three DCs left ventricular hypertrophy, right bundle branch block and normal (ECGs without abnormalities). Training sets with different DC compositions were used for the development of LELT matrices. These matrices were used to estimate the target leads of different test sets. The estimation performance of the developed matrices was quantified using root mean square error values calculated between derived and recorded target leads. Our findings indicate that unbalanced training sets can lead to LELTs that show large estimation performance variability across different DCs. Balanced training sets were found to produce LELTs that performed well across multiple DCs. We recommend balanced training sets for the development of LELTs.
Related Topics
Concepts
Lead (geology)
Composition (language)
Estimation
Set (abstract data type)
Computer science
Training set
Training (meteorology)
Artificial intelligence
Pattern recognition (psychology)
Algorithm
Machine learning
Engineering
Geology
Systems engineering
Programming language
Meteorology
Geomorphology
Linguistics
Philosophy
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.22489/cinc.2023.263
- OA Status
- green
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390446818
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4390446818Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22489/cinc.2023.263Digital Object Identifier
- Title
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Influence of the Training Set Composition on the Estimation Performance of Linear ECG-Lead TransformationsWork 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
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2023-11-26Full publication date if available
- Authors
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Daniel Guldenring, Dewar Finlay, Raymond Bond, Alan Alan Kennedy, Peter Doggart, Ghalib Janjua, James McLaughlinList of authors in order
- Landing page
-
https://doi.org/10.22489/cinc.2023.263Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://rgu-repository.worktribe.com/output/2218670Direct OA link when available
- Concepts
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Lead (geology), Composition (language), Estimation, Set (abstract data type), Computer science, Training set, Training (meteorology), Artificial intelligence, Pattern recognition (psychology), Algorithm, Machine learning, Engineering, Geology, Systems engineering, Programming language, Meteorology, Geomorphology, Linguistics, Philosophy, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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12Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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