Hammer et al., 2024 - Google Patents
Fusion of automatically learned rhythm and morphology features matches diagnostic criteria and enhances AI explainabilityHammer et al., 2024
View PDF- Document ID
- 18068828404343423117
- Author
- Hammer A
- Goettling M
- Malberg H
- Linke A
- Richter S
- Mangner N
- Schmidt M
- Publication year
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Snippet
Deep learning (DL) has demonstrated high accuracy in ECG analysis but lacks in explainability. Although explanations can be estimated using explainable artificial intelligence, their causality has not yet been sufficiently investigated. We present a …
- 230000033764 rhythmic process 0 title abstract description 44
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- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0402—Electrocardiography, i.e. ECG
- A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
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