Sparse representation-based extraction of pulmonary sound components from low-quality auscultation signals
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 a...
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| creator | Sakai, T. Satomoto, H. Kiyasu, S. Miyahara, S. |
| description | 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. |
| doi_str_mv | 10.1109/ICASSP.2012.6287928 |
| format | Conference Proceeding |
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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. 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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.</description><subject>compressed sensing</subject><subject>electronic auscultation</subject><subject>Lungs</subject><subject>Noise</subject><subject>Respiratory system diagnosis</subject><subject>source separation</subject><subject>Sparse matrices</subject><subject>Time frequency analysis</subject><subject>Vectors</subject><subject>Wavelet domain</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>1467300454</isbn><isbn>9781467300452</isbn><isbn>9781467300469</isbn><isbn>1467300446</isbn><isbn>9781467300445</isbn><isbn>1467300462</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kFtLAzEQheMNrLW_oC_5A6lJNpvLoxRvUFCogm9lkk1ky-5mTXbR_ntXqvNyYA7fYeYgtGR0xRg1N0_r2-32ZcUp4yvJtTJcn6CFUZoJqQpKhTSnaMYLZQgz9P0MXf0bpThHM1ZySiQT5hItct7TaSaUFnKG9tseUvY4-T757LsBhjp2xEL2FfbfQwL3u8Ax4H5s2thBOuAcx67CLrZ97CYk45Bii5v4RT5HaOrhgGHMbmyOYTjXHx00-RpdhEn84k_n6O3-7nX9SDbPD9ODG1JzwQaigmKlsdZSzgsHFeNSKQsFCOcAXMmM5EJXEKTzldNSBw3WGSaDLS0XUMzR8phbe-93farb6ebdX23FD_FpYYA</recordid><startdate>201203</startdate><enddate>201203</enddate><creator>Sakai, T.</creator><creator>Satomoto, H.</creator><creator>Kiyasu, S.</creator><creator>Miyahara, S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201203</creationdate><title>Sparse representation-based extraction of pulmonary sound components from low-quality auscultation signals</title><author>Sakai, T. ; Satomoto, H. ; Kiyasu, S. ; Miyahara, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i241t-7f7159bbb0223cad12677ba3a4ccaac5196248daf6cedc868f8abc916fb5b24a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>compressed sensing</topic><topic>electronic auscultation</topic><topic>Lungs</topic><topic>Noise</topic><topic>Respiratory system diagnosis</topic><topic>source separation</topic><topic>Sparse matrices</topic><topic>Time frequency analysis</topic><topic>Vectors</topic><topic>Wavelet domain</topic><toplevel>online_resources</toplevel><creatorcontrib>Sakai, T.</creatorcontrib><creatorcontrib>Satomoto, H.</creatorcontrib><creatorcontrib>Kiyasu, S.</creatorcontrib><creatorcontrib>Miyahara, S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sakai, T.</au><au>Satomoto, H.</au><au>Kiyasu, S.</au><au>Miyahara, S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Sparse representation-based extraction of pulmonary sound components from low-quality auscultation signals</atitle><btitle>2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</btitle><stitle>ICASSP</stitle><date>2012-03</date><risdate>2012</risdate><spage>509</spage><epage>512</epage><pages>509-512</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>1467300454</isbn><isbn>9781467300452</isbn><eisbn>9781467300469</eisbn><eisbn>1467300446</eisbn><eisbn>9781467300445</eisbn><eisbn>1467300462</eisbn><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.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2012.6287928</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, p.509-512 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_6287928 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | compressed sensing electronic auscultation Lungs Noise Respiratory system diagnosis source separation Sparse matrices Time frequency analysis Vectors Wavelet domain |
| title | Sparse representation-based extraction of pulmonary sound components from low-quality auscultation signals |
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