{"id":"https://openalex.org/W4376852374","doi":"https://doi.org/10.1145/3573942.3574118","title":"Improved atrial fibrillation recognition algorithm based on residual network","display_name":"Improved atrial fibrillation recognition algorithm based on residual network","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4376852374","doi":"https://doi.org/10.1145/3573942.3574118"},"language":"en","primary_location":{"id":"doi:10.1145/3573942.3574118","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574118","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","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/A5027759885","display_name":"Zhiqiang Bao","orcid":"https://orcid.org/0000-0001-9858-6916"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiqiang Bao","raw_affiliation_strings":["Xi'an University of Posts & Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0001-9858-6916","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts & Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070191990","display_name":"Ting Ai","orcid":"https://orcid.org/0000-0002-5385-2931"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Ai","raw_affiliation_strings":["Xi'an University of Posts & Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-5385-2931","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts & Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058541397","display_name":"Ying Bai","orcid":"https://orcid.org/0000-0002-4820-5940"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Bai","raw_affiliation_strings":["Xi'an University of Posts & Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-4820-5940","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts & Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027759885"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33526563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"939","last_page":"944"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9761999845504761,"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/T10065","display_name":"Atrial Fibrillation Management and Outcomes","score":0.9671000242233276,"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/residual","display_name":"Residual","score":0.8712410926818848},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7254328727722168},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6491929888725281},{"id":"https://openalex.org/keywords/superposition-principle","display_name":"Superposition principle","score":0.6434752345085144},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5972505211830139},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5021264553070068},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4873674511909485},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48130002617836},{"id":"https://openalex.org/keywords/network-model","display_name":"Network model","score":0.43672409653663635},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4103067219257355},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.214277982711792},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16754373908042908}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8712410926818848},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7254328727722168},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6491929888725281},{"id":"https://openalex.org/C27753989","wikidata":"https://www.wikidata.org/wiki/Q284885","display_name":"Superposition principle","level":2,"score":0.6434752345085144},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5972505211830139},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5021264553070068},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4873674511909485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48130002617836},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.43672409653663635},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4103067219257355},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.214277982711792},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16754373908042908},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3573942.3574118","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574118","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2482102801","https://openalex.org/W2737847855","https://openalex.org/W2862109938","https://openalex.org/W6600116659","https://openalex.org/W6600225990"],"related_works":["https://openalex.org/W4308749380","https://openalex.org/W2037841693","https://openalex.org/W2372481764","https://openalex.org/W1977264433","https://openalex.org/W2746368218","https://openalex.org/W4308448008","https://openalex.org/W2347371661","https://openalex.org/W2170853726","https://openalex.org/W1486810997","https://openalex.org/W4205536354"],"abstract_inverted_index":{"An":[0],"improved":[1],"residual":[2,57,88,102],"network":[3,27,89],"model":[4,32,142,194],"is":[5,59,69,104,128,143],"proposed":[6,174],"to":[7,24,71,84,90,106,130,145,162],"deal":[8],"with":[9,34,75,120],"the":[10,26,31,35,38,56,62,73,76,80,97,100,107,111,123,131,134,138,141,150,159,164,173,190,193,196,199],"complex":[11],"and":[12,64,110,136,182,195],"changeable":[13],"characteristics":[14],"of":[15,30,37,40,47,79,99,113,122,133,140,166,180,186,192,198],"one-dimensional":[16],"electrocardiogram.":[17],"In":[18],"this":[19,114],"model,":[20,135],"firstly,":[21],"in":[22,54],"order":[23],"avoid":[25],"degradation":[28],"problem":[29],"along":[33],"deepening":[36],"number":[39],"layers,":[41],"when":[42],"extracting":[43],"various":[44],"deep-level":[45],"features":[46],"ECG":[48,152],"signals":[49,153],"using":[50],"multiple":[51],"convolution":[52,82],"layers":[53],"CNN,":[55],"module":[58,103],"integrated":[60],"into":[61],"network,":[63],"an":[65],"appropriate":[66],"shortcut":[67],"connection":[68,125],"selected":[70],"connect":[72],"input":[74],"superposition":[77],"output":[78,98],"corresponding":[81],"layer":[83,115],"construct":[85],"a":[86,146,177,183],"deep":[87],"extract":[91],"more":[92],"abstract":[93],"signal":[94],"features.":[95],"Secondly,":[96],"last":[101],"sent":[105],"GAP":[108],"layer,":[109,126],"parameters":[112],"are":[116],"greatly":[117],"reduced":[118],"compared":[119],"those":[121],"full":[124],"which":[127,188],"equivalent":[129],"compression":[132],"thus":[137],"over-fitting":[139],"avoided":[144],"certain":[147],"extent.":[148],"Finally,":[149],"original":[151],"were":[154],"automatically":[155],"classified":[156],"based":[157],"on":[158],"PCinCC2017":[160],"database":[161],"complete":[163],"recognition":[165],"atrial":[167],"fibrillation.":[168],"Experimental":[169],"results":[170],"show":[171],"that":[172],"algorithm":[175],"has":[176],"classification":[178],"accuracy":[179],"86%":[181],"F1":[184],"measure":[185],"83%,":[187],"prove":[189],"feasibility":[191],"effectiveness":[197],"algorithm.":[200]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}