Mykoliuk et al., 2018 - Google Patents
Machine learning methods in ECG classificationMykoliuk et al., 2018
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- 10786827424126068596
- Author
- Mykoliuk I
- Jancarczyk D
- Karpinski M
- Kifer V
- Publication year
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Machine Learning Methods in Electrocardiography Classification Page 1 Machine Learning
Methods in Electrocardiography Classification Iryna Mykoliuk1, Daniel Jancarczyk1, Mikolaj
Karpinski1, Viktor Kifer2 1. Department of Computer Science and Automatics, University of …
Classifications
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- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- 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
- A61B5/046—Detecting fibrillation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- 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
- A61B5/04525—Detecting specific parameters of the electrocardiograph cycle by template matching
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- A61B5/0402—Electrocardiography, i.e. ECG
- A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
- A61B5/0456—Detecting R peaks, e.g. for synchronising diagnostic apparatus
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- A61B5/0468—Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
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