{"id":"https://openalex.org/W2057225119","doi":"https://doi.org/10.1109/bhi.2012.6211744","title":"Embolic doppler ultrasound signal detection using modified dual tree complex wavelet transform","display_name":"Embolic doppler ultrasound signal detection using modified dual tree complex wavelet transform","publication_year":2012,"publication_date":"2012-01-01","ids":{"openalex":"https://openalex.org/W2057225119","doi":"https://doi.org/10.1109/bhi.2012.6211744","mag":"2057225119"},"language":"en","primary_location":{"id":"doi:10.1109/bhi.2012.6211744","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi.2012.6211744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022727512","display_name":"G\u00f6rkem Serbes","orcid":"https://orcid.org/0000-0003-4591-7368"},"institutions":[{"id":"https://openalex.org/I128277893","display_name":"Bah\u00e7e\u015fehir University","ror":"https://ror.org/00yze4d93","country_code":"TR","type":"education","lineage":["https://openalex.org/I128277893"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"G. Serbes","raw_affiliation_strings":["Mechatronics Engineering Department, Bahcesehir University, Istanbul, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mechatronics Engineering Department, Bahcesehir University, Istanbul, Turkey","institution_ids":["https://openalex.org/I128277893"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015969726","display_name":"Nizamettin Ayd\u0131n","orcid":"https://orcid.org/0000-0003-0022-2247"},"institutions":[{"id":"https://openalex.org/I4101805","display_name":"Y\u0131ld\u0131z Technical University","ror":"https://ror.org/0547yzj13","country_code":"TR","type":"education","lineage":["https://openalex.org/I4101805"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"N. Aydin","raw_affiliation_strings":["Computers Engineering Department, Yildiz Technical University, Istanbul, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computers Engineering Department, Yildiz Technical University, Istanbul, Turkey","institution_ids":["https://openalex.org/I4101805"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2256,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.46969194,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"945","last_page":"947"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10816","display_name":"Cerebrovascular and Carotid Artery Diseases","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10924","display_name":"Cardiovascular Health and Disease Prevention","score":0.9747999906539917,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/complex-wavelet-transform","display_name":"Complex wavelet transform","score":0.7727121114730835},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.629212498664856},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.6149821877479553},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5860044360160828},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5806237459182739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5500319600105286},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5273564457893372},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.46185266971588135},{"id":"https://openalex.org/keywords/harmonic-wavelet-transform","display_name":"Harmonic wavelet transform","score":0.43668046593666077},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38291069865226746},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3255048394203186}],"concepts":[{"id":"https://openalex.org/C2777885455","wikidata":"https://www.wikidata.org/wiki/Q5156615","display_name":"Complex wavelet transform","level":5,"score":0.7727121114730835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.629212498664856},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.6149821877479553},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5860044360160828},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5806237459182739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5500319600105286},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5273564457893372},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.46185266971588135},{"id":"https://openalex.org/C1109138","wikidata":"https://www.wikidata.org/wiki/Q3280930","display_name":"Harmonic wavelet transform","level":5,"score":0.43668046593666077},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38291069865226746},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3255048394203186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bhi.2012.6211744","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi.2012.6211744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2006965505","https://openalex.org/W2007876203","https://openalex.org/W2012149499","https://openalex.org/W2013268704","https://openalex.org/W2018332268","https://openalex.org/W2032481619","https://openalex.org/W2057533803","https://openalex.org/W2098633099","https://openalex.org/W2123815656","https://openalex.org/W2127342451","https://openalex.org/W2146842127","https://openalex.org/W2156909104","https://openalex.org/W2172000360","https://openalex.org/W2177031359","https://openalex.org/W6685648643"],"related_works":["https://openalex.org/W1986475093","https://openalex.org/W2085792030","https://openalex.org/W2144408025","https://openalex.org/W2088723847","https://openalex.org/W2120966954","https://openalex.org/W2156522110","https://openalex.org/W2023142747","https://openalex.org/W2205192157","https://openalex.org/W1588899229","https://openalex.org/W1967182499"],"abstract_inverted_index":{"Asymptomatic":[0],"circulating":[1],"cerebral":[2],"emboli,":[3],"which":[4,35,80,131],"are":[5,20,36,195,210],"particles":[6],"larger":[7],"than":[8],"red":[9],"blood":[10],"cells,":[11],"can":[12,28,140],"be":[13,29,110,122,141],"detected":[14],"by":[15,58,181],"Doppler":[16,33],"ultrasound.":[17],"Embolic":[18,26],"signals":[19,27,57,73,116,160,163],"short":[21],"duration":[22],"transient":[23],"like":[24],"signals.":[25],"extracted":[30,173,183],"from":[31,174],"quadrature":[32,42,103,107],"signals,":[34],"obtained":[37],"at":[38],"the":[39,99,106,133,203],"end":[40],"of":[41,55,93,206],"demodulation.":[43],"The":[44],"wavelet":[45,67,77,128],"transform":[46,68,78,85,129],"is":[47,81,89,154],"an":[48,90,102],"ideal":[49],"method":[50],"for":[51,70,178],"analysis":[52],"and":[53,114,117,161,169,191,202],"detection":[54,149,157],"such":[56],"optimizing":[59],"time-frequency":[60],"resolution.":[61],"In":[62,143,156],"literature":[63],"systems":[64],"employing":[65],"discrete":[66],"(DWT)":[69],"detecting":[71],"embolic":[72,159],"exist.":[74],"Dual-tree":[75],"complex":[76,127],"(DTCWT),":[79],"a":[82,146,185],"shift":[83],"invariant":[84],"with":[86],"limited":[87],"redundancy,":[88],"improved":[91],"version":[92],"DWT.":[94],"Conventionally,":[95],"prior":[96],"to":[97,101,137,166],"applying":[98],"DTCWT":[100,120],"signal,":[104],"first":[105],"signal":[108],"must":[109],"decoded":[111],"into":[112,197],"forward":[113],"reverse":[115],"then":[118],"two":[119],"should":[121],"applied.":[123],"However,":[124],"modified":[125],"dual-tree":[126],"(MDTCWT),":[130],"reduces":[132],"computational":[134],"complexity":[135],"compared":[136],"conventional":[138],"algorithm,":[139,158],"used.":[142],"this":[144],"study":[145],"new":[147,186],"emboli":[148],"system":[150],"based":[151],"on":[152],"MDTCWT":[153],"proposed.":[155,211],"artifact":[162],"were":[164,172],"decomposed":[165],"nine":[167],"scales":[168],"different":[170],"features":[171,184],"each":[175,179],"scale.":[176],"Then":[177],"scale,":[180],"using":[182],"feature":[187,193,208],"vector":[188,199],"was":[189],"created":[190],"these":[192],"vectors":[194],"fed":[196],"support":[198],"machines":[200],"individually":[201],"comparative":[204],"results":[205],"individual":[207],"sets":[209]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
