Cross-Domain Transfer Learning for PCG Diagnosis Algorithm.

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Abstrakt

Tytuł:: Cross-Domain Transfer Learning for PCG Diagnosis Algorithm.
Autorzy:: Tseng KK; School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
Wang C; School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
Huang YF; School of Journalism and Communication, Xiamen University, Xiamen 361005, China.
Chen GR; School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
Yung KL; Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Ip WH; Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Źródło:: Biosensors [Biosensors (Basel)] 2021 Apr 20; Vol. 11 (4). Date of Electronic Publication: 2021 Apr 20.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Original Publication: Basel, Switzerland : MDPI Pub.
MeSH Terms:: Monitoring, Physiologic*
Phonocardiography*
Algorithms ; Cardiovascular Diseases ; Humans ; Machine Learning ; Signal Processing, Computer-Assisted ; Sound
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Contributed Indexing:: Keywords: biosignal diagnosis; phonocardiogram; transfer learning
Entry Date(s):: Date Created: 20210430 Date Completed: 20210517 Latest Revision: 20210518
Update Code:: 20240104
PubMed Central ID:: PMC8073829
DOI:: 10.3390/bios11040127
PMID:: 33923928
: Czasopismo naukowe

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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.

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