Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91131
Title: | Cross-domain transfer learning for PCG diagnosis algorithm | Authors: | Tseng, KK Wang, C Huang, YF Chen, GR Yung, KL Ip, WH |
Issue Date: | Apr-2021 | Source: | Biosensors, Apr. 2021, v. 11, no. 4, 127 | 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. | Keywords: | Transfer learning Phonocardiogram Biosignal diagnosis |
Publisher: | MDPI AG | Journal: | Biosensors | EISSN: | 2079-6374 | DOI: | 10.3390/bios11040127 | Rights: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. 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/). 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 |
Appears in Collections: | Journal/Magazine Article |
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Yung_Cross-domain_transfer_learning.pdf | 1.46 MB | Adobe PDF | View/Open |
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