{"id":"https://openalex.org/W2066824565","doi":"https://doi.org/10.4304/jsw.6.7.1257-1264","title":"Study of Hybrid Strategy for Ambulatory ECG Waveform Clustering","display_name":"Study of Hybrid Strategy for Ambulatory ECG Waveform Clustering","publication_year":2011,"publication_date":"2011-07-01","ids":{"openalex":"https://openalex.org/W2066824565","doi":"https://doi.org/10.4304/jsw.6.7.1257-1264","mag":"2066824565"},"language":"en","primary_location":{"id":"doi:10.4304/jsw.6.7.1257-1264","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jsw.6.7.1257-1264","pdf_url":null,"source":{"id":"https://openalex.org/S114141714","display_name":"Journal of Software","issn_l":"1796-217X","issn":["1796-217X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Software","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/A5075963709","display_name":"Gang Zheng","orcid":"https://orcid.org/0000-0002-0705-7398"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gang Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101613895","display_name":"Yu Tian","orcid":"https://orcid.org/0000-0002-3419-2034"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3929,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.69380796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"6","issue":"7","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9937000274658203,"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":0.9937000274658203,"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.9305999875068665,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.91834956407547},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.7335679531097412},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6086419820785522},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3993881344795227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3619700074195862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3252182602882385},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.19399681687355042}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.91834956407547},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.7335679531097412},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6086419820785522},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3993881344795227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3619700074195862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3252182602882385},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.19399681687355042},{"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:10.4304/jsw.6.7.1257-1264","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jsw.6.7.1257-1264","pdf_url":null,"source":{"id":"https://openalex.org/S114141714","display_name":"Journal of Software","issn_l":"1796-217X","issn":["1796-217X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Software","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1480669862","https://openalex.org/W1498214733","https://openalex.org/W1521624597","https://openalex.org/W1554663460","https://openalex.org/W1967608150","https://openalex.org/W1971784203","https://openalex.org/W1982647060","https://openalex.org/W1989946714","https://openalex.org/W1992419399","https://openalex.org/W2004623396","https://openalex.org/W2015245929","https://openalex.org/W2024060531","https://openalex.org/W2043793719","https://openalex.org/W2057691548","https://openalex.org/W2060287725","https://openalex.org/W2065123659","https://openalex.org/W2074530304","https://openalex.org/W2088959061","https://openalex.org/W2096914628","https://openalex.org/W2101603303","https://openalex.org/W2104330778","https://openalex.org/W2117067575","https://openalex.org/W2120178939","https://openalex.org/W2121439886","https://openalex.org/W2125836634","https://openalex.org/W2127253863","https://openalex.org/W2131863438","https://openalex.org/W2132777877","https://openalex.org/W2135885183","https://openalex.org/W2140095548","https://openalex.org/W2141012957","https://openalex.org/W2142458924","https://openalex.org/W2153233077","https://openalex.org/W2154061444","https://openalex.org/W2157690933","https://openalex.org/W2158312857","https://openalex.org/W2169125191","https://openalex.org/W2172181210","https://openalex.org/W2263076435","https://openalex.org/W2319660501","https://openalex.org/W2534504385","https://openalex.org/W2799061466","https://openalex.org/W2923626555","https://openalex.org/W4230417824","https://openalex.org/W4388297464","https://openalex.org/W6692747974"],"related_works":["https://openalex.org/W1974895211","https://openalex.org/W2176409448","https://openalex.org/W2129841057","https://openalex.org/W3040712279","https://openalex.org/W2364769705","https://openalex.org/W4367555392","https://openalex.org/W2374664672","https://openalex.org/W2056136368","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"A":[0],"hybrid":[1],"strategy":[2,46,146],"has":[3],"been":[4],"proposed":[5,45],"to":[6,33,51],"reduce":[7],"the":[8,25,35,53,86,101,110,116,128,140,154],"wrong":[9],"clustering":[10,39,69,107,141,156],"on":[11,92,131],"Ambulatory":[12,16,63],"ECG":[13,17,29,58],"(electrocardiogram).":[14],"Since":[15],"is":[18,41,162],"usually":[19],"composed":[20],"by":[21,66,144,153],"24":[22],"hours":[23],"data,":[24,133],"number":[26],"of":[27,112,119],"individual":[28],"waveform":[30,59],"can":[31,135,147],"reach":[32],"100,000,":[34],"request":[36],"for":[37,125],"accurate":[38,106,150],"result":[40,108,142],"highly":[42],"required.":[43],"The":[44],"adopted":[47,81],"some":[48],"intelligent":[49],"algorithms":[50,89],"solve":[52],"above":[54,87],"problem.":[55],"It":[56],"clusters":[57],"sample":[60],"(selected":[61],"from":[62,85,109],"ECG)":[64],"synchronously":[65],"Max-Min":[67],"distance":[68],"algorithm,":[70],"K-means":[71],"algorithm":[72,76,169,177],"and":[73,171],"Simulated":[74,175],"annealing":[75],"first.":[77],"And":[78],"then,":[79],"it":[80,134],"all":[82],"three":[83,88],"outputs":[84],"as":[90],"input":[91],"Back":[93],"Propagation":[94],"Artificial":[95],"Neural":[96],"Network":[97],"(BP":[98],"ANN).":[99],"In":[100],"end,":[102],"we":[103],"got":[104,148],"more":[105,149],"output":[111],"ANN.":[113],"For":[114],"testing":[115],"results,":[117],"data":[118],"MIT/BIH":[120,132],"arrhythmia":[121],"database":[122],"were":[123],"used":[124],"experiments.":[126],"After":[127],"controlled":[129],"trial":[130],"be":[136],"safely":[137],"concluded":[138],"that":[139,152],"achieved":[143],"improved":[145],"than":[151,167,174],"traditional":[155],"algorithm.":[157],"An":[158],"average":[159],"accuracy":[160],"ratio":[161],"about":[163],"94.6%,":[164],"1.6%":[165],"higher":[166,173],"k-means":[168],"averagely":[170],"1.3%":[172],"Annealing":[176],"averagely.":[178]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
