{"id":"https://openalex.org/W4205954779","doi":"https://doi.org/10.23919/cinc53138.2021.9662903","title":"Deep-Learning Premature Contraction Localization Using Gaussian Based Predicted Data","display_name":"Deep-Learning Premature Contraction Localization Using Gaussian Based Predicted Data","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W4205954779","doi":"https://doi.org/10.23919/cinc53138.2021.9662903"},"language":"en","primary_location":{"id":"doi:10.23919/cinc53138.2021.9662903","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662903","pdf_url":null,"source":{"id":"https://openalex.org/S4363605378","display_name":"2021 Computing in Cardiology (CinC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Computing in Cardiology (CinC)","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/A5010995554","display_name":"Petra Novotn\u00e1","orcid":"https://orcid.org/0000-0003-0018-1975"},"institutions":[{"id":"https://openalex.org/I60587646","display_name":"Brno University of Technology","ror":"https://ror.org/03613d656","country_code":"CZ","type":"education","lineage":["https://openalex.org/I60587646"]}],"countries":["CZ"],"is_corresponding":true,"raw_author_name":"Petra Novotna","raw_affiliation_strings":["Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic","Department of Biomedical Engineering, Faculty of Electrical Engineering and Communications, Brno University of Technology, Brno, Czech Republic"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic","institution_ids":["https://openalex.org/I60587646"]},{"raw_affiliation_string":"Department of Biomedical Engineering, Faculty of Electrical Engineering and Communications, Brno University of Technology, Brno, Czech Republic","institution_ids":["https://openalex.org/I60587646"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101801542","display_name":"Tom\u00e1\u0161 Vi\u010dar","orcid":"https://orcid.org/0000-0002-9136-7873"},"institutions":[{"id":"https://openalex.org/I60587646","display_name":"Brno University of Technology","ror":"https://ror.org/03613d656","country_code":"CZ","type":"education","lineage":["https://openalex.org/I60587646"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Tomas Vicar","raw_affiliation_strings":["Department of Biomedical Engineering, Faculty of Electrical Engineering and Communications, Brno University of Technology, Brno, Czech Republic"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Faculty of Electrical Engineering and Communications, Brno University of Technology, Brno, Czech Republic","institution_ids":["https://openalex.org/I60587646"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086403491","display_name":"Jakub Hej\u010d","orcid":"https://orcid.org/0000-0001-5743-9960"},"institutions":[{"id":"https://openalex.org/I4210136987","display_name":"St. Anne's University Hospital Brno","ror":"https://ror.org/049bjee35","country_code":"CZ","type":"healthcare","lineage":["https://openalex.org/I4210136987"]},{"id":"https://openalex.org/I4210102302","display_name":"University Hospital Brno","ror":"https://ror.org/00qq1fp34","country_code":"CZ","type":"healthcare","lineage":["https://openalex.org/I4210102302"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Jakub Hejc","raw_affiliation_strings":["International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic"],"affiliations":[{"raw_affiliation_string":"International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic","institution_ids":["https://openalex.org/I4210136987","https://openalex.org/I4210102302"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088478548","display_name":"Marina Ronzhina","orcid":null},"institutions":[{"id":"https://openalex.org/I60587646","display_name":"Brno University of Technology","ror":"https://ror.org/03613d656","country_code":"CZ","type":"education","lineage":["https://openalex.org/I60587646"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Marina Ronzhina","raw_affiliation_strings":["Department of Biomedical Engineering, Faculty of Electrical Engineering and Communications, Brno University of Technology, Brno, Czech Republic"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Faculty of Electrical Engineering and Communications, Brno University of Technology, Brno, Czech Republic","institution_ids":["https://openalex.org/I60587646"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010995554"],"corresponding_institution_ids":["https://openalex.org/I60587646"],"apc_list":null,"apc_paid":null,"fwci":0.3451,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47079646,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"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":0.9976000189781189,"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9937999844551086,"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/contraction","display_name":"Contraction (grammar)","score":0.7384543418884277},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6372706890106201},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5963808298110962},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.48153477907180786},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4813278913497925},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4753161072731018},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4444194734096527},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43931955099105835},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.427121102809906},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4247764050960541},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.41856321692466736},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3503764271736145},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.25719714164733887},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11584529280662537},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.10002633929252625},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.08114638924598694}],"concepts":[{"id":"https://openalex.org/C163415756","wikidata":"https://www.wikidata.org/wiki/Q126473","display_name":"Contraction (grammar)","level":2,"score":0.7384543418884277},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6372706890106201},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5963808298110962},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.48153477907180786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4813278913497925},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4753161072731018},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4444194734096527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43931955099105835},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.427121102809906},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4247764050960541},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.41856321692466736},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3503764271736145},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.25719714164733887},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11584529280662537},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.10002633929252625},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.08114638924598694},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/cinc53138.2021.9662903","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662903","pdf_url":null,"source":{"id":"https://openalex.org/S4363605378","display_name":"2021 Computing in Cardiology (CinC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Computing in Cardiology (CinC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2131241448","https://openalex.org/W2194775991","https://openalex.org/W2622380070","https://openalex.org/W2785934082","https://openalex.org/W2888456553","https://openalex.org/W2959582886","https://openalex.org/W2993779232","https://openalex.org/W3082391080","https://openalex.org/W3120302980","https://openalex.org/W4240980180","https://openalex.org/W6678911119","https://openalex.org/W6747701563"],"related_works":["https://openalex.org/W3104422856","https://openalex.org/W4206864338","https://openalex.org/W4287867179","https://openalex.org/W3134690064","https://openalex.org/W4210726438","https://openalex.org/W4287752080","https://openalex.org/W3118984993","https://openalex.org/W3037706579","https://openalex.org/W4401389295","https://openalex.org/W3157580548"],"abstract_inverted_index":{"Detection":[0],"of":[1,59,107,115,142],"cardiac":[2],"arrhythmias":[3],"is":[4,34,97,159],"still":[5],"an":[6],"ongoing":[7],"challenge.":[8],"Here":[9],"we":[10],"focus":[11],"on":[12,36,130],"premature":[13,18],"ventricular":[14],"contraction":[15,20],"(PVC)":[16],"and":[17,22,68,111,151,155],"atrial":[19],"(PAC)":[21],"introduce":[23],"a":[24,81,100],"deep-learning-based":[25],"method":[26,33,127,146],"for":[27,153],"PVC/PAC":[28,49,123],"localization":[29],"in":[30,71],"ECG.":[31],"Our":[32],"based":[35],"involving":[37],"the":[38,46,52,57,63,66,72,87,90,116,121],"time":[39,75],"series":[40,76,94],"with":[41,104],"non-zero":[42,67],"values":[43],"corresponding":[44],"to":[45,86,120],"ground":[47,139],"truth":[48,140],"positions":[50,141],"into":[51],"training":[53],"process.":[54],"To":[55],"improve":[56],"efficiency":[58],"deep":[60],"model":[61],"training,":[62],"transition":[64],"between":[65],"zero":[69],"areas":[70],"train":[73],"output":[74,91],"was":[77,128],"smoothed":[78],"by":[79,99],"introducing":[80],"Gaussian":[82],"function.":[83],"When":[84],"applied":[85],"new":[88],"ECGs,":[89],"signal":[92],"(time":[93],"including":[95],"Gaussians)":[96],"processed":[98],"robust":[101],"peak":[102,112],"detector":[103],"Bayesian":[105],"optimization":[106],"threshold,":[108],"minimal":[109],"distance":[110],"prominence.":[113],"Positions":[114],"detected":[117],"peaks":[118],"correspond":[119],"desired":[122],"positions.":[124],"The":[125,144],"proposed":[126,145],"evaluated":[129],"China":[131],"Physiological":[132],"Signal":[133],"Challenge":[134],"2018":[135],"(CPSC2018)":[136],"using":[137],"own-created":[138],"PVC/PAC.":[143],"reached":[147],"F1":[148],"score":[149],"0.923":[150],"0.688":[152],"PAC":[154],"PVC,":[156],"respectively,":[157],"which":[158],"better":[160],"than":[161],"our":[162],"previous":[163],"results":[164],"obtained":[165],"via":[166],"multiple":[167],"instance":[168],"learning-based":[169],"method.":[170]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
