Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients
Gastric myoelectrical activity can be measured by a noninvasive technique called electrogastrography where surface electrodes are placed on the epigastric area of the abdomen. The electrogastrogram (EGG) signal is by nature a nonstationary signal in terms of its frequency, amplitude and wave shape....
Gespeichert in:
Veröffentlicht in: | Computers in biology and medicine 2006-03, Vol.36 (3), p.276-290 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 290 |
---|---|
container_issue | 3 |
container_start_page | 276 |
container_title | Computers in biology and medicine |
container_volume | 36 |
creator | Kara, Sadık Dirgenali, Fatma Okkesim, Şükrü |
description | Gastric myoelectrical activity can be measured by a noninvasive technique called electrogastrography where surface electrodes are placed on the epigastric area of the abdomen. The electrogastrogram (EGG) signal is by nature a nonstationary signal in terms of its frequency, amplitude and wave shape. Unlike the other methods discrete wavelet analysis (DWT) was designed for nonstationary signals. For automatic assessment of EGG, we used artificial neural networks (ANNs) that have been widely employed in pattern recognition due to their great potential of high performance, flexibility, robust fault tolerance, cost-effective functionality and capability for real-time applications. So we developed a new method for classification of EGG based on DWT and ANN. |
doi_str_mv | 10.1016/j.compbiomed.2005.01.002 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_70719077</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0010482505000089</els_id><sourcerecordid>57625623</sourcerecordid><originalsourceid>FETCH-LOGICAL-c434t-84d3fb91e7ea73aa21a588f369b774d14e1cd2f4f8e93f91770b6cee9b6422113</originalsourceid><addsrcrecordid>eNqFkcFu1DAQhq0KRLeFV6h84pZ0xnbs5FgKBaSqXBZxtBxn0nq1iYOdRdq3b5ZdwXFPc5jvn5H-jzGOUCKgvt2UPg5TG-JAXSkAqhKwBBAXbIW1aQqopHrDVgAIhapFdcmuct4AgAIJ79glaqU0alyx9Weayc8hjjz2_NnlOQXPu31OL_v5ZQiO73IYn_mvNXdjx--enngYeRdcS_MC_g3EySXKIfPJzYHGOb9nb3u3zfThNK_Zz4cv6_tvxeOPr9_v7x4Lr6Sai1p1sm8bJEPOSOcEuqque6mb1hjVoSL0nehVX1Mj-waNgVZ7oqbVSghEec0-Hu9OKf7eUZ7tELKn7daNFHfZGjDYgDFnwcpoUWkhz4LYKLF0e7hYH0GfYs6JejulMLi0twj24Mhu7H9H9uDIAtrF0RK9Of3YtYfdv-BJygJ8OgK0dPcnULLZL7166kJaXNkuhvNfXgG0gqeV</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>19425347</pqid></control><display><type>article</type><title>Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><source>ProQuest Central UK/Ireland</source><creator>Kara, Sadık ; Dirgenali, Fatma ; Okkesim, Şükrü</creator><creatorcontrib>Kara, Sadık ; Dirgenali, Fatma ; Okkesim, Şükrü</creatorcontrib><description>Gastric myoelectrical activity can be measured by a noninvasive technique called electrogastrography where surface electrodes are placed on the epigastric area of the abdomen. The electrogastrogram (EGG) signal is by nature a nonstationary signal in terms of its frequency, amplitude and wave shape. Unlike the other methods discrete wavelet analysis (DWT) was designed for nonstationary signals. For automatic assessment of EGG, we used artificial neural networks (ANNs) that have been widely employed in pattern recognition due to their great potential of high performance, flexibility, robust fault tolerance, cost-effective functionality and capability for real-time applications. So we developed a new method for classification of EGG based on DWT and ANN.</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2005.01.002</identifier><identifier>PMID: 16446161</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Adolescent ; Adult ; Artificial neural network ; Computer applications ; Diabetes Mellitus - physiopathology ; Diagnosis ; Electrocardiography ; Electrogastrography ; Female ; Gastric electrical dysrhythmia ; Gastroparesis - diagnosis ; Gastroparesis - physiopathology ; Health care ; Humans ; Male ; Medicine ; Neural networks ; Neural Networks (Computer) ; Predictive Value of Tests ; Sensitivity and Specificity ; Spectral analysis ; Wavelet transform</subject><ispartof>Computers in biology and medicine, 2006-03, Vol.36 (3), p.276-290</ispartof><rights>2005 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c434t-84d3fb91e7ea73aa21a588f369b774d14e1cd2f4f8e93f91770b6cee9b6422113</citedby><cites>FETCH-LOGICAL-c434t-84d3fb91e7ea73aa21a588f369b774d14e1cd2f4f8e93f91770b6cee9b6422113</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.compbiomed.2005.01.002$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976,64366</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16446161$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kara, Sadık</creatorcontrib><creatorcontrib>Dirgenali, Fatma</creatorcontrib><creatorcontrib>Okkesim, Şükrü</creatorcontrib><title>Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Gastric myoelectrical activity can be measured by a noninvasive technique called electrogastrography where surface electrodes are placed on the epigastric area of the abdomen. The electrogastrogram (EGG) signal is by nature a nonstationary signal in terms of its frequency, amplitude and wave shape. Unlike the other methods discrete wavelet analysis (DWT) was designed for nonstationary signals. For automatic assessment of EGG, we used artificial neural networks (ANNs) that have been widely employed in pattern recognition due to their great potential of high performance, flexibility, robust fault tolerance, cost-effective functionality and capability for real-time applications. So we developed a new method for classification of EGG based on DWT and ANN.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Artificial neural network</subject><subject>Computer applications</subject><subject>Diabetes Mellitus - physiopathology</subject><subject>Diagnosis</subject><subject>Electrocardiography</subject><subject>Electrogastrography</subject><subject>Female</subject><subject>Gastric electrical dysrhythmia</subject><subject>Gastroparesis - diagnosis</subject><subject>Gastroparesis - physiopathology</subject><subject>Health care</subject><subject>Humans</subject><subject>Male</subject><subject>Medicine</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Predictive Value of Tests</subject><subject>Sensitivity and Specificity</subject><subject>Spectral analysis</subject><subject>Wavelet transform</subject><issn>0010-4825</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkcFu1DAQhq0KRLeFV6h84pZ0xnbs5FgKBaSqXBZxtBxn0nq1iYOdRdq3b5ZdwXFPc5jvn5H-jzGOUCKgvt2UPg5TG-JAXSkAqhKwBBAXbIW1aQqopHrDVgAIhapFdcmuct4AgAIJ79glaqU0alyx9Weayc8hjjz2_NnlOQXPu31OL_v5ZQiO73IYn_mvNXdjx--enngYeRdcS_MC_g3EySXKIfPJzYHGOb9nb3u3zfThNK_Zz4cv6_tvxeOPr9_v7x4Lr6Sai1p1sm8bJEPOSOcEuqque6mb1hjVoSL0nehVX1Mj-waNgVZ7oqbVSghEec0-Hu9OKf7eUZ7tELKn7daNFHfZGjDYgDFnwcpoUWkhz4LYKLF0e7hYH0GfYs6JejulMLi0twj24Mhu7H9H9uDIAtrF0RK9Of3YtYfdv-BJygJ8OgK0dPcnULLZL7166kJaXNkuhvNfXgG0gqeV</recordid><startdate>20060301</startdate><enddate>20060301</enddate><creator>Kara, Sadık</creator><creator>Dirgenali, Fatma</creator><creator>Okkesim, Şükrü</creator><general>Elsevier Ltd</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>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>E3H</scope><scope>F2A</scope><scope>7X8</scope></search><sort><creationdate>20060301</creationdate><title>Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients</title><author>Kara, Sadık ; Dirgenali, Fatma ; Okkesim, Şükrü</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c434t-84d3fb91e7ea73aa21a588f369b774d14e1cd2f4f8e93f91770b6cee9b6422113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Artificial neural network</topic><topic>Computer applications</topic><topic>Diabetes Mellitus - physiopathology</topic><topic>Diagnosis</topic><topic>Electrocardiography</topic><topic>Electrogastrography</topic><topic>Female</topic><topic>Gastric electrical dysrhythmia</topic><topic>Gastroparesis - diagnosis</topic><topic>Gastroparesis - physiopathology</topic><topic>Health care</topic><topic>Humans</topic><topic>Male</topic><topic>Medicine</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Predictive Value of Tests</topic><topic>Sensitivity and Specificity</topic><topic>Spectral analysis</topic><topic>Wavelet transform</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kara, Sadık</creatorcontrib><creatorcontrib>Dirgenali, Fatma</creatorcontrib><creatorcontrib>Okkesim, Şükrü</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kara, Sadık</au><au>Dirgenali, Fatma</au><au>Okkesim, Şükrü</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2006-03-01</date><risdate>2006</risdate><volume>36</volume><issue>3</issue><spage>276</spage><epage>290</epage><pages>276-290</pages><issn>0010-4825</issn><eissn>1879-0534</eissn><abstract>Gastric myoelectrical activity can be measured by a noninvasive technique called electrogastrography where surface electrodes are placed on the epigastric area of the abdomen. The electrogastrogram (EGG) signal is by nature a nonstationary signal in terms of its frequency, amplitude and wave shape. Unlike the other methods discrete wavelet analysis (DWT) was designed for nonstationary signals. For automatic assessment of EGG, we used artificial neural networks (ANNs) that have been widely employed in pattern recognition due to their great potential of high performance, flexibility, robust fault tolerance, cost-effective functionality and capability for real-time applications. So we developed a new method for classification of EGG based on DWT and ANN.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>16446161</pmid><doi>10.1016/j.compbiomed.2005.01.002</doi><tpages>15</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0010-4825 |
ispartof | Computers in biology and medicine, 2006-03, Vol.36 (3), p.276-290 |
issn | 0010-4825 1879-0534 |
language | eng |
recordid | cdi_proquest_miscellaneous_70719077 |
source | MEDLINE; Elsevier ScienceDirect Journals; ProQuest Central UK/Ireland |
subjects | Adolescent Adult Artificial neural network Computer applications Diabetes Mellitus - physiopathology Diagnosis Electrocardiography Electrogastrography Female Gastric electrical dysrhythmia Gastroparesis - diagnosis Gastroparesis - physiopathology Health care Humans Male Medicine Neural networks Neural Networks (Computer) Predictive Value of Tests Sensitivity and Specificity Spectral analysis Wavelet transform |
title | Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T14%3A55%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Detection%20of%20gastric%20dysrhythmia%20using%20WT%20and%20ANN%20in%20diabetic%20gastroparesis%20patients&rft.jtitle=Computers%20in%20biology%20and%20medicine&rft.au=Kara,%20Sad%C4%B1k&rft.date=2006-03-01&rft.volume=36&rft.issue=3&rft.spage=276&rft.epage=290&rft.pages=276-290&rft.issn=0010-4825&rft.eissn=1879-0534&rft_id=info:doi/10.1016/j.compbiomed.2005.01.002&rft_dat=%3Cproquest_cross%3E57625623%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=19425347&rft_id=info:pmid/16446161&rft_els_id=S0010482505000089&rfr_iscdi=true |