{"id":"https://openalex.org/W2901104161","doi":"https://doi.org/10.1109/healthcom.2018.8531094","title":"Physiological Waveform Imputation of Missing Data using Convolutional Autoencoders","display_name":"Physiological Waveform Imputation of Missing Data using Convolutional Autoencoders","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2901104161","doi":"https://doi.org/10.1109/healthcom.2018.8531094","mag":"2901104161"},"language":"en","primary_location":{"id":"doi:10.1109/healthcom.2018.8531094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/healthcom.2018.8531094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","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/A5101541021","display_name":"Daniel Miller","orcid":"https://orcid.org/0000-0002-0403-6807"},"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":true,"raw_author_name":"Daniel Miller","raw_affiliation_strings":["Department of Electrical Engineering, Stanford University, Stanford, CA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067727808","display_name":"Andrew Ward","orcid":"https://orcid.org/0000-0002-4692-1126"},"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":"Andrew Ward","raw_affiliation_strings":["Department of Electrical Engineering, Stanford University, Stanford, CA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002056995","display_name":"Nicholas Bambos","orcid":"https://orcid.org/0000-0001-9250-4553"},"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":"Nicholas Bambos","raw_affiliation_strings":["Department of Electrical Engineering, Stanford University, Stanford, CA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080695385","display_name":"David Scheinker","orcid":"https://orcid.org/0000-0001-5885-8024"},"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":"David Scheinker","raw_affiliation_strings":["Department of Management Science and Engineering, Stanford University, Stanford, CA"],"affiliations":[{"raw_affiliation_string":"Department of Management Science and Engineering, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057366552","display_name":"Andrew Shin","orcid":"https://orcid.org/0000-0002-0969-9925"},"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":"Andrew Shin","raw_affiliation_strings":["Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, CA"],"affiliations":[{"raw_affiliation_string":"Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101541021"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":0.8144,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.80157998,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9908999800682068,"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"}},{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9908000230789185,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.8547117710113525},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.8481509685516357},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7951406240463257},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.770679235458374},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.752976655960083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6463104486465454},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6340663433074951},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5219623446464539},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5101099610328674},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47626444697380066},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.42211610078811646},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3699764013290405}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8547117710113525},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8481509685516357},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7951406240463257},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.770679235458374},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.752976655960083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6463104486465454},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6340663433074951},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5219623446464539},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5101099610328674},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47626444697380066},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.42211610078811646},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3699764013290405},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/healthcom.2018.8531094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/healthcom.2018.8531094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","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":31,"referenced_works":["https://openalex.org/W1509813225","https://openalex.org/W1522301498","https://openalex.org/W1537231170","https://openalex.org/W1723414434","https://openalex.org/W2055741845","https://openalex.org/W2101713460","https://openalex.org/W2106104574","https://openalex.org/W2138190513","https://openalex.org/W2145979309","https://openalex.org/W2163390970","https://openalex.org/W2395849284","https://openalex.org/W2405774341","https://openalex.org/W2511968724","https://openalex.org/W2519091744","https://openalex.org/W2731010577","https://openalex.org/W2748379347","https://openalex.org/W2791423513","https://openalex.org/W2899771611","https://openalex.org/W2949382160","https://openalex.org/W2962699674","https://openalex.org/W2963154580","https://openalex.org/W2963341071","https://openalex.org/W2963866024","https://openalex.org/W2964121744","https://openalex.org/W3106063097","https://openalex.org/W6631190155","https://openalex.org/W6675357634","https://openalex.org/W6680455596","https://openalex.org/W6685893538","https://openalex.org/W6740609221","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W2753779043"],"abstract_inverted_index":{"Machine":[0],"Learning":[1],"has":[2],"great":[3],"potential":[4,22],"to":[5,61,131,144,161,167],"improve":[6],"automated":[7],"real-time":[8],"patient":[9,84],"diagnostics.":[10],"For":[11],"the":[12,44,62,103,107,156,163],"majority":[13],"of":[14,20,40,67,106,122],"machine":[15],"learning":[16,92],"algorithms,":[17],"taking":[18],"advantage":[19],"this":[21,142],"requires":[23],"a":[24,38,82,128,168],"complete":[25],"dataset":[26],"with":[27,48],"no":[28],"missing":[29,33,96,149],"data.":[30],"In":[31],"practice,":[32],"values":[34],"are":[35],"estimated":[36],"using":[37,98],"variety":[39],"imputation":[41,56],"methods":[42,146],"in":[43,54,102],"pre-processing":[45],"stage.":[46],"However,":[47],"time-series":[49,150],"data,":[50],"and":[51,65,79,133,140],"physiological":[52,138,165],"waveforms":[53,166],"particular,":[55],"can":[57,94,158],"be":[58,159],"difficult":[59],"due":[60],"unique":[63],"patterns":[64,75,100],"shapes":[66],"each":[68,121],"waveform,":[69],"as":[70,72],"well":[71],"how":[73],"these":[74],"vary":[76],"between":[77],"patients,":[78,125],"even":[80],"for":[81,147],"single":[83],"over":[85],"longer":[86],"durations.":[87],"We":[88,152],"demonstrate":[89],"that":[90,155],"deep":[91],"techniques":[93],"reconstruct":[95],"data":[97],"patient-specific":[99],"present":[101],"non-missing":[104],"portions":[105],"waveform.":[108],"Using":[109],"convolutional":[110],"neural":[111],"network":[112],"(CNN)":[113],"autoencoders":[114],"trained":[115],"on":[116],"288":[117],"15-minute":[118],"samples":[119],"from":[120,136],"138":[123],"pediatric":[124],"we":[126],"develop":[127,145],"generalizable":[129],"model":[130,143],"analyze":[132],"extract":[134],"information":[135],"arbitrary":[137],"waveforms,":[139],"use":[141],"mid-channel":[148],"imputation.":[151],"further":[153],"show":[154],"autoencoder":[157],"used":[160],"compress":[162],"dense":[164],"low-dimensional":[169],"representational":[170],"space.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
