{"id":"https://openalex.org/W4205972839","doi":"https://doi.org/10.23919/cinc53138.2021.9662714","title":"Pathologies Prediction on Short ECG Signals with Focus on Feature Extraction Based on Beat Morphology and Image Deformation","display_name":"Pathologies Prediction on Short ECG Signals with Focus on Feature Extraction Based on Beat Morphology and Image Deformation","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W4205972839","doi":"https://doi.org/10.23919/cinc53138.2021.9662714"},"language":"en","primary_location":{"id":"doi:10.23919/cinc53138.2021.9662714","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662714","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/A5045153817","display_name":"Jeffrey van Prehn","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jeffrey van Prehn","raw_affiliation_strings":["Data Science Consulting Ltd, Pernik, Bulgaria"],"affiliations":[{"raw_affiliation_string":"Data Science Consulting Ltd, Pernik, Bulgaria","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015235263","display_name":"Svetoslav Ivanov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Svetoslav Ivanov","raw_affiliation_strings":["Data Science Consulting Ltd, Pernik, Bulgaria"],"affiliations":[{"raw_affiliation_string":"Data Science Consulting Ltd, Pernik, Bulgaria","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022854931","display_name":"Georgi Nalbantov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Georgi Nalbantov","raw_affiliation_strings":["Data Science Consulting Ltd, Pernik, Bulgaria"],"affiliations":[{"raw_affiliation_string":"Data Science Consulting Ltd, Pernik, Bulgaria","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045153817"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3451,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47433628,"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":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":0.9872999787330627,"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9825000166893005,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7302573919296265},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7072238326072693},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.6379116773605347},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5973319411277771},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5419600009918213},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5252658724784851},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4827989339828491},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4374130070209503},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4254872798919678},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.09267517924308777},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07393959164619446}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7302573919296265},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7072238326072693},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.6379116773605347},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5973319411277771},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5419600009918213},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5252658724784851},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4827989339828491},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4374130070209503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4254872798919678},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.09267517924308777},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07393959164619446},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/cinc53138.2021.9662714","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662714","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":23,"referenced_works":["https://openalex.org/W2061185532","https://openalex.org/W2127913883","https://openalex.org/W2131241448","https://openalex.org/W2162800060","https://openalex.org/W2295598076","https://openalex.org/W2402057639","https://openalex.org/W2888456553","https://openalex.org/W2996959172","https://openalex.org/W3008167346","https://openalex.org/W3027572331","https://openalex.org/W3049106197","https://openalex.org/W3098699929","https://openalex.org/W3119603916","https://openalex.org/W3119974151","https://openalex.org/W3144735448","https://openalex.org/W4206135956","https://openalex.org/W4255272544","https://openalex.org/W6665720480","https://openalex.org/W6678911119","https://openalex.org/W6713211101","https://openalex.org/W6774025570","https://openalex.org/W6788427719","https://openalex.org/W6807079463"],"related_works":["https://openalex.org/W1993278628","https://openalex.org/W2982642565","https://openalex.org/W2113421559","https://openalex.org/W3164111940","https://openalex.org/W100187532","https://openalex.org/W4226366790","https://openalex.org/W3196098778","https://openalex.org/W4382934300","https://openalex.org/W2121061354","https://openalex.org/W4285388059"],"abstract_inverted_index":{"Automated":[0],"detection":[1],"of":[2,30,51,61,65,128,150,168],"key":[3,31,67,97],"cardiac":[4,32,68,98],"pathologies":[5,33],"in":[6,13,23,84,135],"reduced-lead":[7],"ECGs":[8,55,109],"is":[9,48],"an":[10,173],"enabling":[11],"factor":[12],"applying":[14],"ECG":[15],"analysis":[16],"on":[17,79,125,176],"a":[18,28,119,136,146],"larger":[19],"scale.":[20],"The":[21,111,113],"PhysioNet/Computing":[22],"Cardiology":[24],"Challenge":[25],"2021":[26],"identifies":[27],"set":[29],"and":[34,89,155,165,180],"challenges":[35],"us":[36,158],"with":[37],"the":[38,49,59,80,107,126,129],"task":[39,47],"to":[40,45,74],"automatically":[41,75],"detect":[42],"them.":[43],"Critical":[44],"this":[46],"extraction":[50],"features":[52,77],"from":[53,106,110],"these":[54,66,103],"which,":[56],"combined,":[57],"mark":[58],"presence":[60],"one":[62],"or":[63],"more":[64],"pathologies.":[69],"Methodology:":[70],"algorithms":[71],"were":[72,116],"devised":[73],"extract":[76],"based":[78],"definitions":[81],"as":[82],"used":[83],"medical":[85],"practice,":[86],"beat":[87],"morphology":[88],"image":[90],"deformation.":[91],"A":[92],"binary":[93,114,130],"classifier":[94,121],"for":[95,143],"each":[96],"pathology":[99],"was":[100],"trained":[101],"using":[102,132],"features,":[104],"extracted":[105],"labeled":[108],"Challenge.":[112],"classifiers":[115,131],"combined":[117],"into":[118],"multi-label":[120],"by":[122],"learning":[123],"thresholds":[124],"scores":[127],"Bayesian":[133],"optimization":[134],"cross-validation":[137],"setting.":[138],"Results:":[139],"our":[140],"contribution":[141],"submitted":[142,172],"evaluation":[144],"achieved":[145],"challenge":[147],"metric":[148],"score":[149],"0.28,":[151],"0.31,":[152],"0.32,":[153],"0.28":[154],"0.23":[156],"placing":[157],"(team":[159],"DSC)":[160],"29,":[161],"25,":[162,163],"28":[164,166],"out":[167],"39":[169],"teams":[170],"which":[171],"official":[174],"entry":[175],"12-,":[177],"6-,4-,":[178],"3-":[179],"2-lead":[181],"test":[182],"datasets":[183],"respectively.":[184]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}