{"id":"https://openalex.org/W4206506252","doi":"https://doi.org/10.23919/cinc53138.2021.9662875","title":"Cardiac Abnormalities Detection Through 12-Lead/Reduced-Lead ECG Spectrograms and 2D-CRNN","display_name":"Cardiac Abnormalities Detection Through 12-Lead/Reduced-Lead ECG Spectrograms and 2D-CRNN","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W4206506252","doi":"https://doi.org/10.23919/cinc53138.2021.9662875"},"language":"en","primary_location":{"id":"doi:10.23919/cinc53138.2021.9662875","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662875","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/A5022918138","display_name":"Jonathan R. Torres-Castillo","orcid":null},"institutions":[{"id":"https://openalex.org/I135038448","display_name":"Higher Institute of Technologies and Applied Sciences","ror":"https://ror.org/03evg0379","country_code":"CU","type":"education","lineage":["https://openalex.org/I135038448"]},{"id":"https://openalex.org/I4210157894","display_name":"Universidad Instituto Nacional de Ciencias Exactas","ror":"https://ror.org/04rhjk233","country_code":"DO","type":"education","lineage":["https://openalex.org/I4210157894"]},{"id":"https://openalex.org/I8961855","display_name":"Universidad Nacional Aut\u00f3noma de M\u00e9xico","ror":"https://ror.org/01tmp8f25","country_code":"MX","type":"education","lineage":["https://openalex.org/I8961855"]},{"id":"https://openalex.org/I96523616","display_name":"Technological University of Mexico","ror":"https://ror.org/01687hb54","country_code":"MX","type":"education","lineage":["https://openalex.org/I193484058","https://openalex.org/I96523616"]}],"countries":["CU","DO","MX"],"is_corresponding":false,"raw_author_name":"Jonathan R Torres-Castillo","raw_affiliation_strings":["Institute of Applied Sciences and Technology, Universidad Nacional Autónoma de México, Mexico","Instituto de Ciencias Aplicadas y Tecnología (ICAT), UNAM, Cto. Exterior, Cd. Universitaria, Mexico City, Mexico","a (ICAT), UNAM, Cto. Exterior, Cd. Universitaria, Mexico City, Mexico","Institute of Applied Sciences and Technology, Universidad Nacional Aut&#x00F3","Instituto de Ciencias Aplicadas y Tecnolog&#x00ED","noma de M&#x00E9","xico, Mexico"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Applied Sciences and Technology, Universidad Nacional Autónoma de México, Mexico","institution_ids":["https://openalex.org/I4210157894"]},{"raw_affiliation_string":"Instituto de Ciencias Aplicadas y Tecnología (ICAT), UNAM, Cto. Exterior, Cd. Universitaria, Mexico City, Mexico","institution_ids":["https://openalex.org/I96523616"]},{"raw_affiliation_string":"a (ICAT), UNAM, Cto. Exterior, Cd. Universitaria, Mexico City, Mexico","institution_ids":["https://openalex.org/I8961855"]},{"raw_affiliation_string":"Institute of Applied Sciences and Technology, Universidad Nacional Aut&#x00F3","institution_ids":[]},{"raw_affiliation_string":"Instituto de Ciencias Aplicadas y Tecnolog&#x00ED","institution_ids":["https://openalex.org/I135038448"]},{"raw_affiliation_string":"noma de M&#x00E9","institution_ids":[]},{"raw_affiliation_string":"xico, Mexico","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045547694","display_name":"M. Casta\u00f1eda","orcid":"https://orcid.org/0000-0002-1389-2074"},"institutions":[{"id":"https://openalex.org/I135038448","display_name":"Higher Institute of Technologies and Applied Sciences","ror":"https://ror.org/03evg0379","country_code":"CU","type":"education","lineage":["https://openalex.org/I135038448"]},{"id":"https://openalex.org/I8961855","display_name":"Universidad Nacional Aut\u00f3noma de M\u00e9xico","ror":"https://ror.org/01tmp8f25","country_code":"MX","type":"education","lineage":["https://openalex.org/I8961855"]}],"countries":["CU","MX"],"is_corresponding":false,"raw_author_name":"Miguel A Padilla-Castaneda","raw_affiliation_strings":["Institute of Applied Sciences and Technology, Universidad Nacional Aut&#x00F3","noma de M&#x00E9","a (ICAT), UNAM, Cto. Exterior, Cd. Universitaria, Mexico City, Mexico","Instituto de Ciencias Aplicadas y Tecnolog&#x00ED","xico, Mexico"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Applied Sciences and Technology, Universidad Nacional Aut&#x00F3","institution_ids":[]},{"raw_affiliation_string":"noma de M&#x00E9","institution_ids":[]},{"raw_affiliation_string":"a (ICAT), UNAM, Cto. Exterior, Cd. Universitaria, Mexico City, Mexico","institution_ids":["https://openalex.org/I8961855"]},{"raw_affiliation_string":"Instituto de Ciencias Aplicadas y Tecnolog&#x00ED","institution_ids":["https://openalex.org/I135038448"]},{"raw_affiliation_string":"xico, Mexico","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.688,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64452424,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"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":0.9997000098228455,"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":0.9921000003814697,"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":0.9818999767303467,"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/spectrogram","display_name":"Spectrogram","score":0.8346199989318848},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6934587955474854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6471495032310486},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6257059574127197},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6128361225128174},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.57486492395401},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5012261867523193},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.47813689708709717},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4624033570289612},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.44055646657943726}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8346199989318848},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6934587955474854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6471495032310486},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6257059574127197},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6128361225128174},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.57486492395401},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5012261867523193},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.47813689708709717},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4624033570289612},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.44055646657943726},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/cinc53138.2021.9662875","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cinc53138.2021.9662875","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":[{"score":0.8399999737739563,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G8217878963","display_name":null,"funder_award_id":"TA100920","funder_id":"https://openalex.org/F4320335100","funder_display_name":"Direcci\u00f3n General de Asuntos del Personal Acad\u00e9mico, Universidad Nacional Aut\u00f3noma de M\u00e9xico"}],"funders":[{"id":"https://openalex.org/F4320335100","display_name":"Direcci\u00f3n General de Asuntos del Personal Acad\u00e9mico, Universidad Nacional Aut\u00f3noma de M\u00e9xico","ror":"https://ror.org/01tmp8f25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2061441140","https://openalex.org/W2090218979","https://openalex.org/W2809442687","https://openalex.org/W3035916338","https://openalex.org/W3098699929","https://openalex.org/W3099529305","https://openalex.org/W3119243125","https://openalex.org/W3199605009","https://openalex.org/W4206135956","https://openalex.org/W6807079463"],"related_works":["https://openalex.org/W2391959412","https://openalex.org/W2936488316","https://openalex.org/W2059299633","https://openalex.org/W2126100045","https://openalex.org/W2760085659","https://openalex.org/W2732542196","https://openalex.org/W2940977206","https://openalex.org/W2185970706","https://openalex.org/W3003836766","https://openalex.org/W2977314777"],"abstract_inverted_index":{"Heart":[0],"abnormalities":[1],"represent":[2],"around":[3],"32":[4],"%":[5],"of":[6,54,58,87,117,179,184],"the":[7,12,29,80,93,97,118,142,159,164,188],"deaths":[8],"for":[9,22,181],"illnesses":[10],"in":[11,75,123],"world.":[13],"This":[14],"work":[15],"presents":[16],"an":[17,176],"automated":[18],"pattern":[19],"recognition":[20],"method":[21,62,147],"detecting":[23],"25":[24],"different":[25],"cardiac":[26],"arrhythmias":[27],"and":[28,48,71,96],"normal":[30],"sinus":[31],"rhythm":[32],"type.":[33],"A":[34,108,134,144],"two-dimensional":[35],"convolutional":[36],"recurrent":[37,129],"neural":[38,130],"network":[39,110],"(2D-CRNN)":[40],"model":[41],"was":[42,63,111,138,149,155],"employed":[43],"by":[44],"using":[45],"raw-data":[46],"images":[47,88],"signal":[49],"spectrograms.":[50],"With":[51,169],"a":[52,60,124,128],"database":[53],"88,253":[55],"ECG":[56],"signals":[57],"12-leads,":[59],"four-step":[61],"created.":[64],"1.":[65],"Preprocessing.":[66],"The":[67,151],"data":[68],"were":[69,79,89,121],"filtered":[70],"downsample":[72],"or":[73],"filled":[74],"so":[76],"that":[77],"they":[78],"same":[81],"length.":[82],"2.":[83],"Representation.":[84],"Two":[85],"sets":[86,183],"obtained,":[90],"one":[91],"with":[92,99],"time":[94],"series":[95],"other":[98],"spectrograms":[100],"through":[101],"Wavelet":[102],"Synchrosqueezing":[103],"(WS).":[104],"3.":[105],"Feature":[106],"extraction.":[107],"CNN":[109],"chosen":[112],"to":[113,126,140,157,162],"get":[114],"relevant":[115],"features":[116],"images;":[119],"these":[120],"flattened":[122],"vector":[125],"feed":[127],"network.":[131],"4.":[132],"Classification.":[133],"fully":[135],"connected":[136],"layer":[137],"used":[139,156],"classify":[141],"signals.":[143],"10-fold":[145],"cross-validation":[146],"(CV)":[148],"executed.":[150],"grid":[152],"search":[153],"technique":[154],"obtain":[158],"optimal":[160],"threshold":[161],"improve":[163],"Challenge":[165],"Score":[166],"(Ch-score).":[167],"Results:":[168],"this":[170],"model,":[171],"our":[172],"UIDT_UNAM":[173],"team":[174],"received":[175],"unofficial":[177],"score":[178],"0.34":[180],"all":[182],"established":[185],"leads":[186],"utilizing":[187],"hidden":[189],"test":[190],"data.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}