{"id":"https://openalex.org/W3200560766","doi":"https://doi.org/10.1109/mwscas47672.2021.9531856","title":"Waveform Phasicity Prediction from Arterial Sounds through Spectrogram Analysis using Convolutional Neural Networks for Limb Perfusion Assessment","display_name":"Waveform Phasicity Prediction from Arterial Sounds through Spectrogram Analysis using Convolutional Neural Networks for Limb Perfusion Assessment","publication_year":2021,"publication_date":"2021-08-09","ids":{"openalex":"https://openalex.org/W3200560766","doi":"https://doi.org/10.1109/mwscas47672.2021.9531856","mag":"3200560766"},"language":"en","primary_location":{"id":"doi:10.1109/mwscas47672.2021.9531856","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas47672.2021.9531856","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","raw_type":"proceedings-article"},"type":"article","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/A5021079225","display_name":"Adrit Rao","orcid":"https://orcid.org/0000-0002-0780-033X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Adrit Rao","raw_affiliation_strings":["Greene Middle School,Palo Alto,CA,USA","Greene Middle School, Palo Alto, CA, USA"],"affiliations":[{"raw_affiliation_string":"Greene Middle School,Palo Alto,CA,USA","institution_ids":[]},{"raw_affiliation_string":"Greene Middle School, Palo Alto, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053873149","display_name":"Kevin Battenfield","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Battenfield","raw_affiliation_strings":["Stanford University,CA,USA","Stanford University, CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University,CA,USA","institution_ids":["https://openalex.org/I97018004"]},{"raw_affiliation_string":"Stanford University, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086999248","display_name":"Oliver Aalami","orcid":"https://orcid.org/0000-0002-7799-2429"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Oliver Aalami","raw_affiliation_strings":["Stanford University,CA,USA","Stanford University, CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University,CA,USA","institution_ids":["https://openalex.org/I97018004"]},{"raw_affiliation_string":"Stanford University, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021079225"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2913,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60195026,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"8","issue":null,"first_page":"462","last_page":"466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9994999766349792,"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/T11309","display_name":"Music and Audio Processing","score":0.9779999852180481,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11768","display_name":"Music Therapy and Health","score":0.9722999930381775,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.8696246147155762},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.7175941467285156},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6914328336715698},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.656937301158905},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5489438772201538},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5069630146026611},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5003151893615723},{"id":"https://openalex.org/keywords/doppler-effect","display_name":"Doppler effect","score":0.4579765200614929},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44764986634254456},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3791496157646179},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.11196130514144897},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08758494257926941}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8696246147155762},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.7175941467285156},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6914328336715698},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.656937301158905},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5489438772201538},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5069630146026611},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5003151893615723},{"id":"https://openalex.org/C142757262","wikidata":"https://www.wikidata.org/wiki/Q76436","display_name":"Doppler effect","level":2,"score":0.4579765200614929},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44764986634254456},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3791496157646179},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.11196130514144897},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08758494257926941},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mwscas47672.2021.9531856","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas47672.2021.9531856","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1538716300","https://openalex.org/W1773197880","https://openalex.org/W2020997493","https://openalex.org/W2098430424","https://openalex.org/W2108598243","https://openalex.org/W2144344425","https://openalex.org/W2163605009","https://openalex.org/W2168353136","https://openalex.org/W2183341477","https://openalex.org/W2191779130","https://openalex.org/W2617669016","https://openalex.org/W2768091670","https://openalex.org/W2953384591","https://openalex.org/W2964121744","https://openalex.org/W3034476452","https://openalex.org/W4229658977","https://openalex.org/W6631190155","https://openalex.org/W6684191040","https://openalex.org/W6713134421"],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W4375868962","https://openalex.org/W2011227383","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Peripheral":[0],"Arterial":[1],"Disease":[2],"(PAD)":[3],"is":[4,13,169,210],"a":[5,92,120,139,162,226,239],"common":[6],"form":[7],"of":[8,63,68,86,106,179,186,199,204,233,249],"arterial":[9],"occlusive":[10],"disease":[11],"that":[12,96,242],"challenging":[14],"to":[15,28,34,37,46,72,100],"evaluate":[16,29],"at":[17],"the":[18,23,38,43,50,98,155,180,187,216,231,247],"point-of-care.":[19],"Hand-held":[20],"dopplers":[21],"are":[22,53,80],"most":[24],"ubiquitous":[25],"device":[26],"used":[27,170,224],"circulation":[30],"and":[31,70,190,201,214,236],"allows":[32],"providers":[33],"audibly":[35],"\"listen\"":[36],"blood":[39],"flow.":[40],"Providers":[41],"use":[42],"audible":[44],"feedback":[45],"subjectively":[47],"assess":[48],"whether":[49],"sound":[51,137],"characteristics":[52],"consistent":[54],"with":[55,83,127],"Monophasic,":[56],"Biphasic,":[57],"or":[58],"Triphasic":[59],"waveforms.":[60,88],"Subjective":[61],"assessment":[62],"doppler":[64,87,108],"sounds":[65],"raises":[66],"suspicion":[67],"PAD":[69],"leads":[71],"further":[73],"testing,":[74],"often":[75],"delaying":[76],"definitive":[77],"treatment.":[78],"Misdiagnoses":[79],"also":[81],"possible":[82],"subjective":[84],"interpretation":[85],"This":[89,149],"paper":[90],"presents":[91],"Deep":[93,207],"Learning":[94,208],"system":[95,132,194,209,229],"has":[97,215,230],"ability":[99,217],"predict":[101],"waveform":[102],"phasicity":[103,156],"through":[104,173],"analysis":[105],"hand-held":[107],"sounds.":[109],"We":[110],"collected":[111],"268":[112],"four-second":[113],"recordings":[114],"on":[115],"an":[116,196,202],"iPhone":[117],"taken":[118],"during":[119],"formal":[121],"vascular":[122],"lab":[123],"study":[124],"in":[125,146,225,246],"patients":[126],"cardiovascular":[128],"disease.":[129],"Our":[130,206],"end-to-end":[131],"works":[133],"by":[134],"converting":[135],"input":[136],"into":[138],"spectrogram":[140],"which":[141],"visually":[142],"represents":[143],"frequency":[144],"changes":[145,160],"temporal":[147],"patterns.":[148],"conversion":[150],"enables":[151],"visual":[152],"differentiation":[153],"between":[154],"classes.":[157],"With":[158],"these":[159],"present,":[161],"custom":[163],"trained":[164],"Convolutional":[165],"Neural":[166],"Network":[167],"(CNN)":[168],"for":[171,218],"prediction":[172],"learned":[174],"feature":[175],"extraction.":[176],"The":[177,193],"performance":[178],"model":[181],"was":[182],"evaluated":[183],"via":[184],"calculation":[185],"F1":[188,197],"score":[189,198],"accuracy":[191,203],"metrics.":[192],"received":[195],"90.57%":[200],"96.23%.":[205],"not":[211],"computationally":[212],"expensive":[213],"integration":[219],"within":[220],"several":[221],"applications.":[222],"When":[223],"clinic,":[227],"this":[228],"capability":[232],"preventing":[234],"misdiagnosis":[235],"gives":[237],"practitioners":[238],"second":[240],"opinion":[241],"can":[243],"be":[244],"useful":[245],"evaluation":[248],"PAD.":[250]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}