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Lin et al., 2020 - Google Patents

Explainable deep neural network for identifying cardiac abnormalities using class activation map

Lin et al., 2020

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Document ID
8060886596082604877
Author
Lin Y
Lee Y
Tsai W
Beh W
Wu A
Publication year
Publication venue
2020 Computing in Cardiology

External Links

Snippet

In this study, our team “NTU-Accesslab” present a deep convolutional neural network (CNN) approach, called CNN-GAP, for classifying 12-lead ECGs with multilabel cardiac abnormalities. Additionally, Class Activation Mapping (CAM) is employed for further …
Continue reading at physionet.org (PDF) (other versions)

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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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    • A61B5/04012—Analysis of electro-cardiograms, electro-encephalograms, electro-myograms
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    • A—HUMAN NECESSITIES
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    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0531—Measuring skin impedance
    • A—HUMAN NECESSITIES
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    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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