{"id":"https://openalex.org/W2798146972","doi":"https://doi.org/10.1109/bhi.2018.8333406","title":"P-QRS-T localization in ECG using deep learning","display_name":"P-QRS-T localization in ECG using deep learning","publication_year":2018,"publication_date":"2018-03-01","ids":{"openalex":"https://openalex.org/W2798146972","doi":"https://doi.org/10.1109/bhi.2018.8333406","mag":"2798146972"},"language":"en","primary_location":{"id":"doi:10.1109/bhi.2018.8333406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi.2018.8333406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE EMBS International Conference on Biomedical &amp; Health Informatics (BHI)","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/A5027894662","display_name":"Hedayat Abrishami","orcid":null},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hedayat Abrishami","raw_affiliation_strings":["Department of EECS, University of Cincinnati, Cincinnati, OH, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of EECS, University of Cincinnati, Cincinnati, OH, USA","institution_ids":["https://openalex.org/I63135867"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064915779","display_name":"Matthew Campbell","orcid":"https://orcid.org/0000-0002-2311-6482"},"institutions":[{"id":"https://openalex.org/I1335321130","display_name":"Children's Hospital of Philadelphia","ror":"https://ror.org/01z7r7q48","country_code":"US","type":"funder","lineage":["https://openalex.org/I1335321130"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Campbell","raw_affiliation_strings":["Division of Cardiology at Children's Hospital of Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Cardiology at Children's Hospital of Philadelphia, PA, USA","institution_ids":["https://openalex.org/I1335321130"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058545944","display_name":"Chia Y. Han","orcid":"https://orcid.org/0000-0002-3935-9155"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chia Han","raw_affiliation_strings":["Department of EECS, University of Cincinnati, Cincinnati, OH, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of EECS, University of Cincinnati, Cincinnati, OH, USA","institution_ids":["https://openalex.org/I63135867"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055688279","display_name":"Richard J. Czosek","orcid":"https://orcid.org/0000-0001-7497-9555"},"institutions":[{"id":"https://openalex.org/I1285204247","display_name":"Cincinnati Children's Hospital Medical Center","ror":"https://ror.org/01hcyya48","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1285204247"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard Czosek","raw_affiliation_strings":["Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA","institution_ids":["https://openalex.org/I1285204247"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102211681","display_name":"Xuefu Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuefu Zhou","raw_affiliation_strings":["Department of EECS, University of Cincinnati, Cincinnati, OH, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of EECS, University of Cincinnati, Cincinnati, OH, USA","institution_ids":["https://openalex.org/I63135867"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9632,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.91992991,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"210","last_page":"213"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998999834060669,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10217","display_name":"Cardiac electrophysiology and arrhythmias","score":0.9886000156402588,"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/qrs-complex","display_name":"QRS complex","score":0.7659183740615845},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.7531018853187561},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7038190960884094},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6719754934310913},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.670750617980957},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6198909282684326},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.531640887260437},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5297216773033142},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.49324339628219604},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4555756151676178},{"id":"https://openalex.org/keywords/qt-interval","display_name":"QT interval","score":0.4222135543823242},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41433292627334595},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.11952149868011475},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10850116610527039},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09615719318389893},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.07938772439956665},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.06838327646255493}],"concepts":[{"id":"https://openalex.org/C111773187","wikidata":"https://www.wikidata.org/wiki/Q1969239","display_name":"QRS complex","level":2,"score":0.7659183740615845},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7531018853187561},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7038190960884094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6719754934310913},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.670750617980957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6198909282684326},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.531640887260437},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5297216773033142},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.49324339628219604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4555756151676178},{"id":"https://openalex.org/C118441451","wikidata":"https://www.wikidata.org/wiki/Q12074763","display_name":"QT interval","level":2,"score":0.4222135543823242},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41433292627334595},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.11952149868011475},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10850116610527039},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09615719318389893},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.07938772439956665},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.06838327646255493},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bhi.2018.8333406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi.2018.8333406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE EMBS International Conference on Biomedical &amp; Health Informatics (BHI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1849277567","https://openalex.org/W1975050716","https://openalex.org/W2024504058","https://openalex.org/W2077887234","https://openalex.org/W2082439039","https://openalex.org/W2090472274","https://openalex.org/W2095705004","https://openalex.org/W2110235300","https://openalex.org/W2112796928","https://openalex.org/W2114432210","https://openalex.org/W2117539524","https://openalex.org/W2127510558","https://openalex.org/W2132300419","https://openalex.org/W2146950261","https://openalex.org/W2162273778","https://openalex.org/W2162800060","https://openalex.org/W2163605009","https://openalex.org/W2291961022","https://openalex.org/W2618530766","https://openalex.org/W2964121744","https://openalex.org/W6631190155","https://openalex.org/W6674330103"],"related_works":["https://openalex.org/W4312200629","https://openalex.org/W4223943233","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4309045103","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W4280592718","https://openalex.org/W3186919929","https://openalex.org/W4312863455"],"abstract_inverted_index":{"This":[0,89,132],"paper":[1],"describes":[2],"a":[3,18,28,33,81,104],"work":[4],"using":[5],"the":[6,72,84,98,109,115,126,143,175,178,194],"capabilities":[7],"of":[8,27,35,62,75,97,121,123,135,177],"deep":[9,99],"neural":[10,100],"networks":[11,101],"to":[12,58,107,138,173,192],"predict":[13,114],"key":[14,49],"wave":[15,116,185],"locations":[16],"in":[17,46,68,83,125,183],"cardiac":[19,63,77,144],"complex":[20],"on":[21,38],"an":[22,169],"electrocardiogram":[23],"(ECG)":[24],"as":[25],"part":[26],"challenge":[29,82],"introduced":[30],"by":[31],"Physionet,":[32],"provider":[34],"ECG":[36,85],"collections,":[37],"detecting":[39],"critical":[40,76],"waveforms":[41],"that":[42,112],"contain":[43],"essential":[44],"information":[45],"cardiology.":[47],"The":[48],"waves":[50,78],"include":[51],"P-wave,":[52],"QRS-wave,":[53],"and":[54,94,102,164],"T-wave.":[55],"Recent":[56],"attempts":[57],"extract":[59],"hierarchical":[60],"features":[61],"complexes":[64,145],"have":[65],"been":[66,80,130],"reported":[67],"literature,":[69],"but":[70],"finding":[71],"accurate":[73],"position":[74],"has":[79,129],"signal":[86],"processing":[87],"research.":[88],"study":[90,133],"investigates":[91],"multiple":[92,151],"architectures":[93,190],"learning":[95],"rates":[96],"adopts":[103],"four-step":[105],"procedure":[106],"find":[108],"best":[110],"one":[111],"can":[113],"locations.":[117],"A":[118],"remarkable":[119],"rate":[120],"96.2%":[122],"accuracy":[124,176],"localization":[127],"task":[128],"achieved.":[131],"consists":[134],"four":[136],"parts":[137],"produce":[139],"output":[140],"predictions;":[141],"obtaining":[142],"from":[146],"QT":[147],"Databse":[148],"(QTDB);":[149],"introduce":[150],"architectures,":[152],"including":[153],"fully-connected":[154],"networks,":[155],"LeNet-style":[156,160],"ConvNet":[157,161],"with":[158,180],"dropout,":[159],"without":[162],"dropout":[163],"train":[165],"these":[166,189],"networks;":[167],"use":[168],"unseen":[170],"test":[171],"set":[172],"calculate":[174],"system":[179],"different":[181],"tolerance":[182],"each":[184],"interval;":[186],"compare":[187],"all":[188],"together":[191],"analyze":[193],"most":[195],"suitable":[196],"architecture":[197],"for":[198],"this":[199],"task.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
