Matches in SemOpenAlex for { <https://semopenalex.org/work/W4310117754> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W4310117754 endingPage "104444" @default.
- W4310117754 startingPage "104444" @default.
- W4310117754 abstract "Over the years, significant hospitals have scanned paper electrocardiograms and saved them into electronic health records to form digital management of diagnosis and treatment records. Therefore, the electrocardiogram(ECG) scans have substantial sample diversity, correlate with the patient's past medical records, and have significant research value. However, the scanned ECG is noisy, and it is difficult to directly use it as the training data of the intelligent diagnosis algorithm, so it is necessary to preprocess it. In recent years, some researchers have proposed methods to extract ECG signals from noisy ECG using neural networks, but the results are not good enough due to the lack of paired noise ECG and noiseless ECG to train neural networks. This paper proposes an unsupervised noise ECG image generation method to overcome this difficulty. These generated images have the same ECG signals as the input ECG image, and the noise is similar to the actual ECG image. These two kinds of input images do not need to be paired. To evaluate the global and local quality of the generated images, our method uses a two-discriminator generative adversarial network and calls them global discriminator and local discriminator separately. The output vector of the global discriminator is fed into the generator when training the model, thereby helping the generator to improve the generated results in a targeted manner. Using the data generated by this method to train the neural network for ECG signal extraction can significantly improve its extraction performance. The Dice coefficient of the proposed network reaches 0.868, which is higher than 0.798 of the robust baseline model. Therefore, the proposed method can effectively solve the problem of lacking training data in the current ECG signal extraction network." @default.
- W4310117754 created "2022-11-30" @default.
- W4310117754 creator A5018286656 @default.
- W4310117754 creator A5034962322 @default.
- W4310117754 creator A5049078993 @default.
- W4310117754 date "2023-03-01" @default.
- W4310117754 modified "2023-10-16" @default.
- W4310117754 title "Noise ECG generation method based on generative adversarial network" @default.
- W4310117754 cites W1901129140 @default.
- W4310117754 cites W1975640002 @default.
- W4310117754 cites W2041337722 @default.
- W4310117754 cites W2057836191 @default.
- W4310117754 cites W2075628710 @default.
- W4310117754 cites W2081005579 @default.
- W4310117754 cites W2162800060 @default.
- W4310117754 cites W2562637781 @default.
- W4310117754 cites W2620656322 @default.
- W4310117754 cites W2760318868 @default.
- W4310117754 cites W2802832784 @default.
- W4310117754 cites W2954996726 @default.
- W4310117754 cites W2962793481 @default.
- W4310117754 cites W2963073614 @default.
- W4310117754 cites W2963800363 @default.
- W4310117754 cites W2967737346 @default.
- W4310117754 cites W2977063526 @default.
- W4310117754 cites W2980819778 @default.
- W4310117754 cites W3001683732 @default.
- W4310117754 cites W3012755169 @default.
- W4310117754 cites W3094845650 @default.
- W4310117754 cites W3125658501 @default.
- W4310117754 cites W3155894390 @default.
- W4310117754 cites W3170826323 @default.
- W4310117754 cites W4255631470 @default.
- W4310117754 cites W4283770671 @default.
- W4310117754 doi "https://doi.org/10.1016/j.bspc.2022.104444" @default.
- W4310117754 hasPublicationYear "2023" @default.
- W4310117754 type Work @default.
- W4310117754 citedByCount "1" @default.
- W4310117754 countsByYear W43101177542023 @default.
- W4310117754 crossrefType "journal-article" @default.
- W4310117754 hasAuthorship W4310117754A5018286656 @default.
- W4310117754 hasAuthorship W4310117754A5034962322 @default.
- W4310117754 hasAuthorship W4310117754A5049078993 @default.
- W4310117754 hasConcept C115961682 @default.
- W4310117754 hasConcept C121332964 @default.
- W4310117754 hasConcept C153180895 @default.
- W4310117754 hasConcept C154945302 @default.
- W4310117754 hasConcept C163258240 @default.
- W4310117754 hasConcept C2779803651 @default.
- W4310117754 hasConcept C2780992000 @default.
- W4310117754 hasConcept C41008148 @default.
- W4310117754 hasConcept C50644808 @default.
- W4310117754 hasConcept C62520636 @default.
- W4310117754 hasConcept C76155785 @default.
- W4310117754 hasConcept C94915269 @default.
- W4310117754 hasConcept C99498987 @default.
- W4310117754 hasConceptScore W4310117754C115961682 @default.
- W4310117754 hasConceptScore W4310117754C121332964 @default.
- W4310117754 hasConceptScore W4310117754C153180895 @default.
- W4310117754 hasConceptScore W4310117754C154945302 @default.
- W4310117754 hasConceptScore W4310117754C163258240 @default.
- W4310117754 hasConceptScore W4310117754C2779803651 @default.
- W4310117754 hasConceptScore W4310117754C2780992000 @default.
- W4310117754 hasConceptScore W4310117754C41008148 @default.
- W4310117754 hasConceptScore W4310117754C50644808 @default.
- W4310117754 hasConceptScore W4310117754C62520636 @default.
- W4310117754 hasConceptScore W4310117754C76155785 @default.
- W4310117754 hasConceptScore W4310117754C94915269 @default.
- W4310117754 hasConceptScore W4310117754C99498987 @default.
- W4310117754 hasFunder F4320328119 @default.
- W4310117754 hasFunder F4320335787 @default.
- W4310117754 hasLocation W43101177541 @default.
- W4310117754 hasOpenAccess W4310117754 @default.
- W4310117754 hasPrimaryLocation W43101177541 @default.
- W4310117754 hasRelatedWork W2904616728 @default.
- W4310117754 hasRelatedWork W2946952476 @default.
- W4310117754 hasRelatedWork W2974859913 @default.
- W4310117754 hasRelatedWork W3105928910 @default.
- W4310117754 hasRelatedWork W3119479239 @default.
- W4310117754 hasRelatedWork W4224217118 @default.
- W4310117754 hasRelatedWork W4281389463 @default.
- W4310117754 hasRelatedWork W4283584549 @default.
- W4310117754 hasRelatedWork W4308217387 @default.
- W4310117754 hasRelatedWork W4366112369 @default.
- W4310117754 hasVolume "81" @default.
- W4310117754 isParatext "false" @default.
- W4310117754 isRetracted "false" @default.
- W4310117754 workType "article" @default.