Paper Info
Title
Sparse representation-based extraction of pulmonary sound components from low-quality auscultation signals
Abstract
Toward assistance of respiratory system diagnosis, sparse representation of auscultation signals is utilized to extract pulmonary sound components. This signal extraction is a challenging task because the pulmonary sounds such as vesicular sounds and crackles are overlapping each other in the time and frequency domains, and they are so faint that the quality of recorded signals is quite low in many cases. It is experimentally shown that the pulmonary sound components are successfully extracted from low-quality auscultation signals via the sparse representation. This extraction method is confirmed to be highly robust against random noise and digital quantization.
Year
DOI
Venue
2012
10.1109/ICASSP.2012.6287928
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
acoustic signal processing,medical computing,patient diagnosis,signal representation,ultrasonic applications,crackles,digital quantization,frequency domains,low-quality auscultation signals,pulmonary sound component extraction,pulmonary sound components,random noise,recorded signals,respiratory system diagnosis,signal extraction,sparse representation-based extraction,time domains,vesicular sounds,Respiratory system diagnosis,compressed sensing,electronic auscultation,source separation
Crackles,Pattern recognition,Computer science,Sparse approximation,Speech recognition,Time–frequency analysis,Artificial intelligence,Auscultation,Quantization (signal processing),Compressed sensing,Sparse matrix,Source separation
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4673-0044-5
978-1-4673-0044-5
0
PageRank 
References 
Authors
0.34
10
4
Name
Order
Citations
PageRank
Tomoya Sakai100.34
Haruka Satomoto200.34
Senya Kiyasu342.10
Sueharu Miyahara4407.47