{"id":"https://openalex.org/W2343281336","doi":"https://doi.org/10.1109/bhi.2016.7455877","title":"False arrhythmia alarm reduction in the intensive care unit using data fusion and machine learning","display_name":"False arrhythmia alarm reduction in the intensive care unit using data fusion and machine learning","publication_year":2016,"publication_date":"2016-02-01","ids":{"openalex":"https://openalex.org/W2343281336","doi":"https://doi.org/10.1109/bhi.2016.7455877","mag":"2343281336"},"language":"en","primary_location":{"id":"doi:10.1109/bhi.2016.7455877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi.2016.7455877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE-EMBS International Conference on Biomedical and 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/A5100381917","display_name":"Qiang Zhang","orcid":"https://orcid.org/0000-0002-4746-0392"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I4210110458","display_name":"Institute of Electronics","ror":"https://ror.org/01z143507","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210110458"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiang Zhang","raw_affiliation_strings":["Institute of Electronics, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Electronics, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210110458","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005915865","display_name":"Xianxiang Chen","orcid":"https://orcid.org/0000-0002-9525-6089"},"institutions":[{"id":"https://openalex.org/I4210110458","display_name":"Institute of Electronics","ror":"https://ror.org/01z143507","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210110458"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianxiang Chen","raw_affiliation_strings":["Institute of Electronics, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Electronics, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210110458","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108416437","display_name":"Zhen Fang","orcid":"https://orcid.org/0000-0002-2976-3242"},"institutions":[{"id":"https://openalex.org/I4210110458","display_name":"Institute of Electronics","ror":"https://ror.org/01z143507","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210110458"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Fang","raw_affiliation_strings":["Institute of Electronics, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Electronics, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210110458","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041822714","display_name":"Shanhong Xia","orcid":"https://orcid.org/0000-0001-7231-8147"},"institutions":[{"id":"https://openalex.org/I4210110458","display_name":"Institute of Electronics","ror":"https://ror.org/01z143507","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210110458"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanhong Xia","raw_affiliation_strings":["Institute of Electronics, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Electronics, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210110458","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100381917"],"corresponding_institution_ids":["https://openalex.org/I4210110458","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.6132,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.71407471,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"232","last_page":"235"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13248","display_name":"Healthcare Technology and Patient Monitoring","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T13248","display_name":"Healthcare Technology and Patient Monitoring","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9969000220298767,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/photoplethysmogram","display_name":"Photoplethysmogram","score":0.5676007270812988},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5632686614990234},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5623375177383423},{"id":"https://openalex.org/keywords/cardiac-arrhythmia","display_name":"Cardiac arrhythmia","score":0.5599551796913147},{"id":"https://openalex.org/keywords/ventricular-tachycardia","display_name":"Ventricular tachycardia","score":0.5502672791481018},{"id":"https://openalex.org/keywords/asystole","display_name":"Asystole","score":0.5406877398490906},{"id":"https://openalex.org/keywords/heart-rate-variability","display_name":"Heart rate variability","score":0.5068393349647522},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4926672577857971},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.4788255989551544},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47837844491004944},{"id":"https://openalex.org/keywords/bradycardia","display_name":"Bradycardia","score":0.46896305680274963},{"id":"https://openalex.org/keywords/ventricular-fibrillation","display_name":"Ventricular fibrillation","score":0.4430280327796936},{"id":"https://openalex.org/keywords/heart-rate","display_name":"Heart rate","score":0.40836477279663086},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.35057204961776733},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.2850208878517151},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.26358386874198914},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.21618050336837769},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2025781273841858},{"id":"https://openalex.org/keywords/atrial-fibrillation","display_name":"Atrial fibrillation","score":0.1806803047657013},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10777679085731506}],"concepts":[{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.5676007270812988},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5632686614990234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5623375177383423},{"id":"https://openalex.org/C2988455589","wikidata":"https://www.wikidata.org/wiki/Q189331","display_name":"Cardiac arrhythmia","level":3,"score":0.5599551796913147},{"id":"https://openalex.org/C2776331378","wikidata":"https://www.wikidata.org/wiki/Q56002","display_name":"Ventricular tachycardia","level":2,"score":0.5502672791481018},{"id":"https://openalex.org/C2776818218","wikidata":"https://www.wikidata.org/wiki/Q752800","display_name":"Asystole","level":2,"score":0.5406877398490906},{"id":"https://openalex.org/C71635504","wikidata":"https://www.wikidata.org/wiki/Q933954","display_name":"Heart rate variability","level":4,"score":0.5068393349647522},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4926672577857971},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.4788255989551544},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47837844491004944},{"id":"https://openalex.org/C2777495988","wikidata":"https://www.wikidata.org/wiki/Q217111","display_name":"Bradycardia","level":4,"score":0.46896305680274963},{"id":"https://openalex.org/C2781005686","wikidata":"https://www.wikidata.org/wiki/Q848662","display_name":"Ventricular fibrillation","level":2,"score":0.4430280327796936},{"id":"https://openalex.org/C2777953023","wikidata":"https://www.wikidata.org/wiki/Q1073121","display_name":"Heart rate","level":3,"score":0.40836477279663086},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.35057204961776733},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.2850208878517151},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.26358386874198914},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.21618050336837769},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2025781273841858},{"id":"https://openalex.org/C2779161974","wikidata":"https://www.wikidata.org/wiki/Q815819","display_name":"Atrial fibrillation","level":2,"score":0.1806803047657013},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10777679085731506},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bhi.2016.7455877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi.2016.7455877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1568380872","https://openalex.org/W2024479929","https://openalex.org/W2033877969","https://openalex.org/W2109197536","https://openalex.org/W2115814469","https://openalex.org/W2141785376","https://openalex.org/W2146915726","https://openalex.org/W2153635508","https://openalex.org/W2163624457","https://openalex.org/W2236428329","https://openalex.org/W2291378922","https://openalex.org/W3120421331","https://openalex.org/W3151043256","https://openalex.org/W4245196771","https://openalex.org/W6634102894","https://openalex.org/W6680801884"],"related_works":["https://openalex.org/W2414946388","https://openalex.org/W2755336558","https://openalex.org/W2082775165","https://openalex.org/W4379141680","https://openalex.org/W2290895961","https://openalex.org/W2965577416","https://openalex.org/W2490293253","https://openalex.org/W2947444833","https://openalex.org/W2478248559","https://openalex.org/W2343281336"],"abstract_inverted_index":{"With":[0],"aim":[1],"of":[2,6,69,92,98,155,168,184,197],"reducing":[3],"the":[4,67,70,153,158,174,182,188],"incidence":[5],"false":[7],"critical":[8],"arrhythmia":[9,61,160],"alarms":[10,156],"in":[11,25,31,37,106,187],"intensive":[12],"care":[13],"units,":[14],"a":[15,95,134,143,194],"novel":[16],"data":[17],"fusion":[18],"and":[19,112,126,132],"machine":[20,146],"learning":[21],"algorithm":[22,141,170,192],"is":[23,171],"presented":[24],"this":[26,38,191],"article.":[27],"The":[28,162],"2015":[29,189],"PhysioNet/Computing":[30],"Cardiology":[32],"Challenge":[33],"database":[34],"was":[35,72,149],"used":[36],"present":[39],"algorithm,":[40],"with":[41,173],"each":[42],"grouped":[43],"as":[44,116],"an":[45],"asystole":[46],"(AS),":[47],"extreme":[48,51],"bradycardia":[49],"(EB),":[50],"tachycardia":[52,55],"(ET),":[53],"ventricular":[54,58],"(VT)":[56],"or":[57],"flutter/fibrillation":[59],"(VF)":[60],"alarm.":[62],"A":[63],"10-second":[64],"segment":[65],"before":[66],"onset":[68],"alarm":[71],"truncated":[73],"from":[74],"available":[75,93],"signals,":[76,94],"namely":[77],"electrocardiogram":[78],"(ECG),":[79],"arterial":[80],"blood":[81],"pressure":[82],"(ABP),":[83],"and/or":[84],"photoplethysmogram":[85],"(PPG).":[86],"By":[87],"first":[88],"assessing":[89],"signal":[90],"quality":[91],"robust":[96],"estimation":[97],"beat-to-beat":[99],"intervals":[100],"could":[101],"then":[102],"be":[103],"derived.":[104],"Features":[105],"heart":[107],"rate":[108,166,177],"variability":[109],"(HRV)":[110],"analysis":[111,121],"ECG":[113],"parameters":[114],"such":[115],"temporal":[117],"statistical":[118],"parameters,":[119],"spectral":[120],"results,":[122],"wavelet":[123],"transformation":[124],"coefficients,":[125],"complexity":[127],"measurement":[128],"etc":[129],"were":[130],"extracted":[131],"formed":[133],"vector.":[135],"After":[136],"feature":[137],"selection":[138],"through":[139],"genetic":[140],"(GA),":[142],"support":[144],"vector":[145],"(SVM)":[147],"model":[148],"applied":[150],"to":[151,181],"conduct":[152],"classification":[154,169],"for":[157],"specific":[159],"type.":[161],"overall":[163],"true":[164,175],"positive":[165],"(TPR)":[167],"93%,":[172],"negative":[176],"(TNR)":[178],"94%.":[179],"According":[180],"method":[183],"performance":[185],"evaluation":[186],"Challenge,":[190],"achieved":[193],"gross":[195],"score":[196],"84.4.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
