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Liu et al., 2018 - Google Patents

Automatic identification of abnormalities in 12-lead ECGs using expert features and convolutional neural networks

Liu et al., 2018

Document ID
965899035441079447
Author
Liu Z
Meng X
Cui J
Huang Z
Wu J
Publication year
Publication venue
2018 International Conference on Sensor Networks and Signal Processing (SNSP)

External Links

Snippet

Automatic identification of the rhythm/morphology abnormalities in ECGs has gained growing attention in various areas and remains a challenge. We propose an algorithm to classify 12-lead ECGs into 9 categories. We extracted expert features including generic …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

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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
    • A61B5/0468—Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
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    • A61B5/046—Detecting fibrillation
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    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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