{"id":"https://openalex.org/W4286656361","doi":"https://doi.org/10.1109/jiot.2022.3188771","title":"Lead Separation and Combination: A Novel Unsupervised 12-Lead ECG Feature Learning Framework for Internet of Medical Things","display_name":"Lead Separation and Combination: A Novel Unsupervised 12-Lead ECG Feature Learning Framework for Internet of Medical Things","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4286656361","doi":"https://doi.org/10.1109/jiot.2022.3188771"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2022.3188771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2022.3188771","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-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/A5062915713","display_name":"Wenhan Liu","orcid":"https://orcid.org/0000-0002-0920-6908"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhan Liu","raw_affiliation_strings":["School of Physics and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-0920-6908","affiliations":[{"raw_affiliation_string":"School of Physics and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011171494","display_name":"Qianxi Guo","orcid":"https://orcid.org/0000-0003-0831-6336"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianxi Guo","raw_affiliation_strings":["School of Physics and Technology, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Physics and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101842901","display_name":"Xinwei Gao","orcid":"https://orcid.org/0009-0008-7399-601X"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinwei Gao","raw_affiliation_strings":["School of Physics and Technology, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Physics and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102006180","display_name":"Sheng Chang","orcid":"https://orcid.org/0000-0003-4875-5501"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Chang","raw_affiliation_strings":["School of Physics and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-4875-5501","affiliations":[{"raw_affiliation_string":"School of Physics and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446023","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0001-5279-3645"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["School of Physics and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-5279-3645","affiliations":[{"raw_affiliation_string":"School of Physics and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101838024","display_name":"Jin He","orcid":"https://orcid.org/0000-0002-8747-0472"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin He","raw_affiliation_strings":["School of Physics and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-8747-0472","affiliations":[{"raw_affiliation_string":"School of Physics and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055120491","display_name":"Qijun Huang","orcid":"https://orcid.org/0000-0001-9679-5191"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qijun Huang","raw_affiliation_strings":["School of Physics and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-9679-5191","affiliations":[{"raw_affiliation_string":"School of Physics and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6804,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.85149413,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"9","issue":"23","first_page":"23897","last_page":"23914"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":1.0,"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":1.0,"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":0.9976999759674072,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.7422953248023987},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5811519622802734},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.49498218297958374},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46320831775665283},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4613957107067108},{"id":"https://openalex.org/keywords/lead","display_name":"Lead (geology)","score":0.45525890588760376},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4314134120941162},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4128565490245819},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.41173580288887024},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36642834544181824},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34885525703430176}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7422953248023987},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5811519622802734},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.49498218297958374},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46320831775665283},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4613957107067108},{"id":"https://openalex.org/C2777093003","wikidata":"https://www.wikidata.org/wiki/Q6508345","display_name":"Lead (geology)","level":2,"score":0.45525890588760376},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4314134120941162},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4128565490245819},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.41173580288887024},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36642834544181824},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34885525703430176},{"id":"https://openalex.org/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2022.3188771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2022.3188771","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1633187807","display_name":null,"funder_award_id":"61874079","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2785318553","display_name":null,"funder_award_id":"62074116","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G856552681","display_name":null,"funder_award_id":"81971702","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1943579973","https://openalex.org/W1972142091","https://openalex.org/W1988183757","https://openalex.org/W2047181473","https://openalex.org/W2074187626","https://openalex.org/W2114842946","https://openalex.org/W2115356273","https://openalex.org/W2139322118","https://openalex.org/W2152790380","https://openalex.org/W2152808281","https://openalex.org/W2187089797","https://openalex.org/W2251133041","https://openalex.org/W2273832011","https://openalex.org/W2527796983","https://openalex.org/W2563686712","https://openalex.org/W2568858846","https://openalex.org/W2702116941","https://openalex.org/W2740884108","https://openalex.org/W2755499309","https://openalex.org/W2765690220","https://openalex.org/W2794550444","https://openalex.org/W2797694788","https://openalex.org/W2804642894","https://openalex.org/W2842511635","https://openalex.org/W2887119478","https://openalex.org/W2888456553","https://openalex.org/W2902644322","https://openalex.org/W2913789442","https://openalex.org/W2916066245","https://openalex.org/W2919115771","https://openalex.org/W2964036440","https://openalex.org/W2980825080","https://openalex.org/W2982096173","https://openalex.org/W3005680577","https://openalex.org/W3006339384","https://openalex.org/W3023371261","https://openalex.org/W3027572331","https://openalex.org/W3035524453","https://openalex.org/W3035965352","https://openalex.org/W3093635124","https://openalex.org/W3095753783","https://openalex.org/W3099878876","https://openalex.org/W3101926760","https://openalex.org/W3108655343","https://openalex.org/W3125937743","https://openalex.org/W3127637041","https://openalex.org/W3130904508","https://openalex.org/W3135891860","https://openalex.org/W3137222725","https://openalex.org/W3138270768","https://openalex.org/W3153100805","https://openalex.org/W3163645474","https://openalex.org/W3166413374","https://openalex.org/W3172243723","https://openalex.org/W3186495029","https://openalex.org/W3188409561","https://openalex.org/W3207038235","https://openalex.org/W4230914943","https://openalex.org/W4287775015","https://openalex.org/W4297775537","https://openalex.org/W4297808394","https://openalex.org/W6637373629","https://openalex.org/W6682948231","https://openalex.org/W6737664043","https://openalex.org/W6769439433","https://openalex.org/W6774314701","https://openalex.org/W6785972966","https://openalex.org/W6794173828"],"related_works":["https://openalex.org/W4220926404","https://openalex.org/W3123344745","https://openalex.org/W3174759195","https://openalex.org/W3167013339","https://openalex.org/W4287121366","https://openalex.org/W3048601286","https://openalex.org/W2965925734","https://openalex.org/W60493759","https://openalex.org/W4309346246","https://openalex.org/W4308619659"],"abstract_inverted_index":{"The":[0,91,201,216],"development":[1],"of":[2,7,34,49,84,132,237],"healthcare":[3],"industry,":[4],"especially":[5],"Internet":[6],"Medical":[8],"Things":[9],"(IoMT),":[10],"has":[11,203],"generated":[12],"considerable":[13],"unlabeled":[14,29],"electrocardiogram":[15],"(ECG)":[16],"signals.":[17],"This":[18],"article":[19],"proposes":[20],"a":[21,32,81,207],"new":[22],"unsupervised":[23,104],"feature":[24,105],"learning":[25],"method":[26,239],"for":[27,67,103,138,149,193,198,240],"these":[28],"12-lead":[30,50,99],"ECGs,":[31],"type":[33],"12-channel":[35],"1-D":[36],"time":[37],"series.":[38],"Based":[39],"on":[40,206],"contrastive":[41],"predictive":[42],"coding":[43],"(CPC),":[44],"it":[45,185],"considers":[46],"the":[47,74,116,133,144,187,226,231,235],"characteristics":[48],"ECGs":[51],"and":[52,58,76,86,127,161,172,177,196,243],"develops":[53],"novel":[54],"lead-separation":[55],"CPC":[56,60],"(LSCPC)":[57],"lead-combination":[59],"(LCCPC).":[61],"Specifically,":[62],"LSCPC":[63,85],"captures":[64],"intralead":[65],"features":[66],"each":[68],"lead,":[69],"while":[70],"LCCPC":[71,87],"combines":[72],"all":[73,230],"leads":[75],"explores":[77],"interlead":[78],"relationships.":[79],"Furthermore,":[80],"fusion":[82],"model":[83,169,202],"generates":[88],"final":[89],"representations.":[90],"Physikalisch-Technische":[92],"Bundesanstalt":[93],"(PTB)-XL":[94],"database":[95,135,146],"that":[96],"contains":[97],"21837":[98],"records":[100,121,142],"is":[101,219],"used":[102,137,148],"learning.":[106],"Using":[107,165],"learned":[108],"features,":[109],"linear":[110],"classifiers":[111],"are":[112,136,147],"trained":[113],"to":[114],"accomplish":[115],"downstream":[117],"tasks.":[118],"448":[119],"ECG":[120],"from":[122,143],"148":[123],"myocardial":[124],"infarction":[125],"(MI)":[126],"52":[128],"healthy":[129],"control":[130],"subjects":[131],"PTB":[134],"MI":[139,176,194],"detection.":[140,200],"6877":[141],"CPSC-2018":[145],"atrial":[150],"fibrillation":[151],"(AF)":[152],"detection,":[153,179],"including":[154],"918":[155],"normal":[156],"records,":[157,160],"1098":[158],"AF":[159,178,199],"4861":[162],"other":[163],"records.":[164],"fivefold":[166],"cross-validation,":[167],"our":[168,238],"achieves":[170],"90.38%":[171],"73.27%":[173],"accuracy":[174],"in":[175],"respectively.":[180],"Compared":[181],"with":[182],"existing":[183],"models,":[184],"improves":[186],"performances":[188],"by":[189],"at":[190],"least":[191],"2.32%":[192],"detection":[195],"3.99%":[197],"been":[204],"deployed":[205],"lightweight":[208,244],"embedded":[209],"system":[210],"(800-MHz":[211],"ARM":[212],"processor,":[213],"1-GB":[214],"RAM).":[215],"maximum":[217],"latency":[218],"only":[220],"465.34":[221],"ms,":[222],"which":[223],"can":[224],"satisfy":[225],"real-time":[227],"constraints.":[228],"Overall,":[229],"results":[232],"have":[233],"demonstrated":[234],"potential":[236],"real-world":[241],"healthcare,":[242],"IoMT":[245],"applications.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}