Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91131
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | - |
dc.creator | Tseng, KK | - |
dc.creator | Wang, C | - |
dc.creator | Huang, YF | - |
dc.creator | Chen, GR | - |
dc.creator | Yung, KL | - |
dc.creator | Ip, WH | - |
dc.date.accessioned | 2021-09-09T03:39:59Z | - |
dc.date.available | 2021-09-09T03:39:59Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/91131 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | en_US |
dc.rights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Tseng, K.-K.; Wang, C.; Huang, Y.-F.; Chen, G.-R.; Yung, K.-L.; Ip, W.-H. Cross-Domain Transfer Learning for PCG Diagnosis Algorithm. Biosensors 2021, 11, 127 is available at https://doi.org/10.3390/bios11040127 | en_US |
dc.subject | Transfer learning | en_US |
dc.subject | Phonocardiogram | en_US |
dc.subject | Biosignal diagnosis | en_US |
dc.title | Cross-domain transfer learning for PCG diagnosis algorithm | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 11 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.3390/bios11040127 | - |
dcterms.abstract | Cardiechema is a way to reflect cardiovascular disease where the doctor uses a stethoscope to help determine the heart condition with a sound map. In this paper, phonocardiogram (PCG) is used as a diagnostic signal, and a deep learning diagnostic framework is proposed. By improving the architecture and modules, a new transfer learning and boosting architecture is mainly employed. In addition, a segmentation method is designed to improve on the existing signal segmentation methods, such as R wave to R wave interval segmentation and fixed segmentation. For the evaluation, the final diagnostic architecture achieved a sustainable performance with a public PCG database. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Biosensors, Apr. 2021, v. 11, no. 4, 127 | - |
dcterms.isPartOf | Biosensors | - |
dcterms.issued | 2021-04 | - |
dc.identifier.isi | WOS:000642771400001 | - |
dc.identifier.pmid | 33923928 | - |
dc.identifier.eissn | 2079-6374 | - |
dc.identifier.artn | 127 | - |
dc.description.validate | 202109 bchy | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Yung_Cross-domain_transfer_learning.pdf | 1.46 MB | Adobe PDF | View/Open |
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