Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform
Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature...
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| Veröffentlicht in: | International journal of biological sciences 2018-01, Vol.14 (8), p.938-945 |
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| container_title | International journal of biological sciences |
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| creator | Shi, Yan Wang, Guoliang Niu, Jinglong Zhang, Qimin Cai, Maolin Sun, Baoqing Wang, Dandan Xue, Mei Zhang, Xiaohua Douglas |
| description | Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature extraction of sputum sound signals using the wavelet transform and classification of sputum existence using artificial neural network (ANN). Sputum sound signals were decomposed into the frequency subbands using the wavelet transform. A set of features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN system, trained using the Back Propagation (BP) algorithm, was implemented to recognize the existence of sputum sounds. The maximum precision rate of automatic recognition in texture of signals was as high as 84.53%. This study can be referred to as the optimization of performance and design in the automatic technology for sputum detection using sputum sound signals. |
| doi_str_mv | 10.7150/ijbs.23855 |
| format | Article |
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To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature extraction of sputum sound signals using the wavelet transform and classification of sputum existence using artificial neural network (ANN). Sputum sound signals were decomposed into the frequency subbands using the wavelet transform. A set of features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN system, trained using the Back Propagation (BP) algorithm, was implemented to recognize the existence of sputum sounds. The maximum precision rate of automatic recognition in texture of signals was as high as 84.53%. This study can be referred to as the optimization of performance and design in the automatic technology for sputum detection using sputum sound signals.</description><identifier>ISSN: 1449-2288</identifier><identifier>EISSN: 1449-2288</identifier><identifier>DOI: 10.7150/ijbs.23855</identifier><identifier>PMID: 29989104</identifier><language>eng</language><publisher>Australia: Ivyspring International Publisher Pty Ltd</publisher><subject>Acoustics ; Algorithms ; Artificial neural networks ; Back propagation ; Classification ; Design optimization ; Feature extraction ; Humans ; Mechanical ventilation ; Neural networks ; Neural Networks, Computer ; Research Paper ; Respiratory System ; Secretion ; Sound ; Sputum ; Sputum - physiology ; Texture recognition ; Ventilation ; Wave propagation ; Wavelet Analysis ; Wavelet transforms</subject><ispartof>International journal of biological sciences, 2018-01, Vol.14 (8), p.938-945</ispartof><rights>Copyright BioMed Central 2018</rights><rights>Ivyspring International Publisher 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c406t-40ce9ecfef5e6b1c02dbb3eed632d52fa404b6bc9a73c6dfb927a5b28404e8c33</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036751/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036751/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29989104$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shi, Yan</creatorcontrib><creatorcontrib>Wang, Guoliang</creatorcontrib><creatorcontrib>Niu, Jinglong</creatorcontrib><creatorcontrib>Zhang, Qimin</creatorcontrib><creatorcontrib>Cai, Maolin</creatorcontrib><creatorcontrib>Sun, Baoqing</creatorcontrib><creatorcontrib>Wang, Dandan</creatorcontrib><creatorcontrib>Xue, Mei</creatorcontrib><creatorcontrib>Zhang, Xiaohua Douglas</creatorcontrib><title>Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform</title><title>International journal of biological sciences</title><addtitle>Int J Biol Sci</addtitle><description>Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature extraction of sputum sound signals using the wavelet transform and classification of sputum existence using artificial neural network (ANN). Sputum sound signals were decomposed into the frequency subbands using the wavelet transform. A set of features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN system, trained using the Back Propagation (BP) algorithm, was implemented to recognize the existence of sputum sounds. The maximum precision rate of automatic recognition in texture of signals was as high as 84.53%. This study can be referred to as the optimization of performance and design in the automatic technology for sputum detection using sputum sound signals.</description><subject>Acoustics</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Back propagation</subject><subject>Classification</subject><subject>Design optimization</subject><subject>Feature extraction</subject><subject>Humans</subject><subject>Mechanical ventilation</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Research Paper</subject><subject>Respiratory System</subject><subject>Secretion</subject><subject>Sound</subject><subject>Sputum</subject><subject>Sputum - physiology</subject><subject>Texture recognition</subject><subject>Ventilation</subject><subject>Wave propagation</subject><subject>Wavelet Analysis</subject><subject>Wavelet transforms</subject><issn>1449-2288</issn><issn>1449-2288</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkU9LJDEQxYOs6Kh78QMsgb2IMJpOOun0ZUGG9Q-IHnTYY0jSFc1sdzKbdLv47e3RcZjdUxVVPx6v6iF0XJCzquDk3C9MPqNMcr6DJkVZ1lNKpfyy1e-jg5wXhDDBJdlD-7SuZV2QcoLms1bn7J23uvcx4Ojww3Lohw4_xCE0Gc-zD0_4IvUrxusW38GQ3kv_N6bfWIcG_9Iv0EKPH5MO2cXUHaFdp9sMX9f1EM0vfz7Orqe391c3s4vbqS2J6KclsVCDdeA4CFNYQhtjGEAjGG04dbokpRHG1rpiVjTO1LTS3FA5zkFaxg7Rjw_d5WA6aCyEfvSmlsl3Or2qqL36dxP8s3qKL0qMr6h4MQqcrAVS_DNA7lXns4W21QHikBUlopKy5NUK_f4fuohDCuN5IyUZl1RwMlKnH5RNMecEbmOmIGqVllqlpd7TGuFv2_Y36Gc87A15CZKm</recordid><startdate>20180101</startdate><enddate>20180101</enddate><creator>Shi, Yan</creator><creator>Wang, Guoliang</creator><creator>Niu, Jinglong</creator><creator>Zhang, Qimin</creator><creator>Cai, Maolin</creator><creator>Sun, Baoqing</creator><creator>Wang, Dandan</creator><creator>Xue, Mei</creator><creator>Zhang, Xiaohua Douglas</creator><general>Ivyspring International Publisher Pty Ltd</general><general>Ivyspring International Publisher</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7QO</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20180101</creationdate><title>Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform</title><author>Shi, Yan ; Wang, Guoliang ; Niu, Jinglong ; Zhang, Qimin ; Cai, Maolin ; Sun, Baoqing ; Wang, Dandan ; Xue, Mei ; Zhang, Xiaohua Douglas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c406t-40ce9ecfef5e6b1c02dbb3eed632d52fa404b6bc9a73c6dfb927a5b28404e8c33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Acoustics</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Back propagation</topic><topic>Classification</topic><topic>Design optimization</topic><topic>Feature extraction</topic><topic>Humans</topic><topic>Mechanical ventilation</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Research Paper</topic><topic>Respiratory System</topic><topic>Secretion</topic><topic>Sound</topic><topic>Sputum</topic><topic>Sputum - physiology</topic><topic>Texture recognition</topic><topic>Ventilation</topic><topic>Wave propagation</topic><topic>Wavelet Analysis</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shi, Yan</creatorcontrib><creatorcontrib>Wang, Guoliang</creatorcontrib><creatorcontrib>Niu, Jinglong</creatorcontrib><creatorcontrib>Zhang, Qimin</creatorcontrib><creatorcontrib>Cai, Maolin</creatorcontrib><creatorcontrib>Sun, Baoqing</creatorcontrib><creatorcontrib>Wang, Dandan</creatorcontrib><creatorcontrib>Xue, Mei</creatorcontrib><creatorcontrib>Zhang, Xiaohua Douglas</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of biological sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shi, Yan</au><au>Wang, Guoliang</au><au>Niu, Jinglong</au><au>Zhang, Qimin</au><au>Cai, Maolin</au><au>Sun, Baoqing</au><au>Wang, Dandan</au><au>Xue, Mei</au><au>Zhang, Xiaohua Douglas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform</atitle><jtitle>International journal of biological sciences</jtitle><addtitle>Int J Biol Sci</addtitle><date>2018-01-01</date><risdate>2018</risdate><volume>14</volume><issue>8</issue><spage>938</spage><epage>945</epage><pages>938-945</pages><issn>1449-2288</issn><eissn>1449-2288</eissn><abstract>Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature extraction of sputum sound signals using the wavelet transform and classification of sputum existence using artificial neural network (ANN). Sputum sound signals were decomposed into the frequency subbands using the wavelet transform. A set of features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN system, trained using the Back Propagation (BP) algorithm, was implemented to recognize the existence of sputum sounds. The maximum precision rate of automatic recognition in texture of signals was as high as 84.53%. 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| subjects | Acoustics Algorithms Artificial neural networks Back propagation Classification Design optimization Feature extraction Humans Mechanical ventilation Neural networks Neural Networks, Computer Research Paper Respiratory System Secretion Sound Sputum Sputum - physiology Texture recognition Ventilation Wave propagation Wavelet Analysis Wavelet transforms |
| title | Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform |
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