{"id":"https://openalex.org/W4409760183","doi":"https://doi.org/10.13053/cys-29-1-5527","title":"Atrial Fibrillation Classification Using a Deep Spectral Autoencoder","display_name":"Atrial Fibrillation Classification Using a Deep Spectral Autoencoder","publication_year":2025,"publication_date":"2025-04-01","ids":{"openalex":"https://openalex.org/W4409760183","doi":"https://doi.org/10.13053/cys-29-1-5527"},"language":"es","primary_location":{"id":"doi:10.13053/cys-29-1-5527","doi":"https://doi.org/10.13053/cys-29-1-5527","is_oa":false,"landing_page_url":"https://doi.org/10.13053/cys-29-1-5527","pdf_url":null,"source":{"id":"https://openalex.org/S61446325","display_name":"Computaci\u00f3n y Sistemas","issn_l":"1405-5546","issn":["1405-5546","2007-9737"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","latindex","norway-1","poland-40","russia-white-list-4","scielo"],"host_organization":"https://openalex.org/P4310319599","host_organization_name":"National Polytechnic Institute","host_organization_lineage":["https://openalex.org/P4310319599"],"host_organization_lineage_names":["National Polytechnic Institute"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computaci\u00f3n y Sistemas","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5093201441","display_name":"Enrique Quezada-Pr\u00f3spero","orcid":"https://orcid.org/0000-0001-5954-4481","observed_orcids":["https://orcid.org/0000-0001-5954-4481"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Enrique Quezada-Pr\u00f3spero","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009976088","display_name":"Dante M\u00fajica\u2010Vargas","orcid":"https://orcid.org/0000-0001-8665-4096","observed_orcids":["https://orcid.org/0000-0001-8665-4096"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dante M\u00fajica-Vargas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094276171","display_name":"Luis A. Cruz-Pr\u00f3spero","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luis A. Cruz-Pr\u00f3spero","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015704367","display_name":"Christian Garc\u00eda-Aquino","orcid":"https://orcid.org/0000-0001-5008-0043","observed_orcids":["https://orcid.org/0000-0001-5008-0043"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Christian Garc\u00eda-Aquino","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075315183","display_name":"Arturo Rend\u00f3n-Castro","orcid":"https://orcid.org/0000-0003-4210-7677","observed_orcids":["https://orcid.org/0000-0003-4210-7677"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u00c1ngel A. Rend\u00f3n-Castro","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":"1","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":0.9990000128746033,"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":0.9990000128746033,"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/T10217","display_name":"Cardiac electrophysiology and arrhythmias","score":9.999999747378752e-05,"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/T10667","display_name":"Emotion and Mood Recognition","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/atrial-fibrillation-classification","display_name":"atrial fibrillation classification","score":0.8980000019073486},{"id":"https://openalex.org/keywords/deep-autoencoder","display_name":"deep autoencoder","score":0.8309999704360962},{"id":"https://openalex.org/keywords/spectral-properties","display_name":"spectral properties","score":0.8949999809265137},{"id":"https://openalex.org/keywords/ecg-signals","display_name":"ECG signals","score":0.7480000257492065},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.7910000085830688},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.42800000309944153}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8728988170623779},{"id":"https://openalex.org/C2779161974","wikidata":"https://www.wikidata.org/wiki/Q815819","display_name":"Atrial fibrillation","level":2,"score":0.7310491800308228},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4753955602645874},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.43540728092193604},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.4281829595565796},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.406647264957428},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.36890536546707153},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32289451360702515},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2433892786502838}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.13053/cys-29-1-5527","doi":"https://doi.org/10.13053/cys-29-1-5527","is_oa":false,"landing_page_url":"https://doi.org/10.13053/cys-29-1-5527","pdf_url":null,"source":{"id":"https://openalex.org/S61446325","display_name":"Computaci\u00f3n y Sistemas","issn_l":"1405-5546","issn":["1405-5546","2007-9737"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","latindex","norway-1","poland-40","russia-white-list-4","scielo"],"host_organization":"https://openalex.org/P4310319599","host_organization_name":"National Polytechnic Institute","host_organization_lineage":["https://openalex.org/P4310319599"],"host_organization_lineage_names":["National Polytechnic Institute"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computaci\u00f3n y Sistemas","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5683000087738037}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2915030573","https://openalex.org/W4303614315","https://openalex.org/W4384157127","https://openalex.org/W3023855696","https://openalex.org/W2884579557","https://openalex.org/W3016893795","https://openalex.org/W2972620123","https://openalex.org/W4386004296","https://openalex.org/W2795178393","https://openalex.org/W2945636339"],"abstract_inverted_index":{"This":[0],"study":[1],"introduces":[2],"a":[3,10,14],"novel":[4],"approach":[5],"foratrial":[6],"fibrillation":[7,59],"classification":[8],"using":[9,103],"deep":[11],"autoencoderstacked":[12],"with":[13,22],"fully":[15,89],"connected":[16,90],"softmax":[17],"layer.":[18],"Themodel":[19],"is":[20,35],"trained":[21],"spectral":[23,29,65],"features":[24,69,83],"extracted":[25],"fromECG":[26],"signals":[27,62],"through":[28,97],"and":[30,79,123],"signal":[31],"analysis.The":[32],"primary":[33],"goal":[34],"to":[36,71,87],"enhance":[37],"existing":[38],"algorithms":[39],"inthe":[40],"state-of-the-art":[41,102],"by":[42],"delivering":[43],"superior":[44],"results":[45,110],"withreduced":[46],"computational":[47],"cost.":[48],"The":[49,61,81,107],"PhysioNet":[50],"Challenge2017":[51],"database":[52],"was":[53,95],"used,":[54],"which":[55,75],"contains":[56],"normal":[57],"andatrial":[58],"rhythms.":[60],"were":[63,84],"normalizedbefore":[64],"feature":[66,77],"extraction.":[67],"These":[68],"wereused":[70],"train":[72],"the":[73,101],"autoencoder,":[74],"performedadditional":[76],"extraction":[78],"dimensionalityreduction.":[80],"resulting":[82],"then":[85],"used":[86],"trainthe":[88],"layer":[91],"responsible":[92],"for":[93],"classification.Performance":[94],"evaluated":[96],"quality":[98],"metricsmentioned":[99],"in":[100],"cross-validationto":[104],"ensure":[105],"robustness.":[106],"best":[108],"median":[109],"obtainedwere:":[111],"99.7%":[112,119],"Accuracy,":[113],"99.8%":[114],"Precision,":[115],"99.5%":[116],"Recall,99.8%":[117],"Specificity,":[118],"F1-score,":[120],"99.3%":[121,124],"MatthewsCorrelation,":[122],"Kappa.":[125]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2025-04-25T00:00:00"}
