{"id":"https://openalex.org/W4386437742","doi":"https://doi.org/10.1093/comnet/cnad030","title":"Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning","display_name":"Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning","publication_year":2023,"publication_date":"2023-09-05","ids":{"openalex":"https://openalex.org/W4386437742","doi":"https://doi.org/10.1093/comnet/cnad030"},"language":"en","primary_location":{"id":"doi:10.1093/comnet/cnad030","is_oa":false,"landing_page_url":"https://doi.org/10.1093/comnet/cnad030","pdf_url":null,"source":{"id":"https://openalex.org/S2531583244","display_name":"Journal of Complex Networks","issn_l":"2051-1310","issn":["2051-1310","2051-1329"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Complex Networks","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/A5047140493","display_name":"Aruane M. Pineda","orcid":"https://orcid.org/0000-0002-4469-5049"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Aruane M Pineda","raw_affiliation_strings":["Institute of Mathematical and Computer Sciences (ICMC), University of S\u00e3o Paulo (USP) , S\u00e3o Paulo, 13566-590, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-4469-5049","affiliations":[{"raw_affiliation_string":"Institute of Mathematical and Computer Sciences (ICMC), University of S\u00e3o Paulo (USP) , S\u00e3o Paulo, 13566-590, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012685144","display_name":"Caroline L. Alves","orcid":"https://orcid.org/0000-0003-4708-1330"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]},{"id":"https://openalex.org/I4210158205","display_name":"Aschaffenburg University of Applied Sciences","ror":"https://ror.org/04sms9203","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210158205"]}],"countries":["BR","DE"],"is_corresponding":false,"raw_author_name":"Caroline L Alves","raw_affiliation_strings":["Institute of Mathematical and Computer Sciences (ICMC), University of S\u00e3o Paulo (USP), S\u00e3o Paulo, 13566-590, Brazil and Aschaffenburg University of Applied Sciences, Laboratory for Hybrid Modeling , Aschaffenburg 63743, Germany"],"raw_orcid":"https://orcid.org/0000-0003-4708-1330","affiliations":[{"raw_affiliation_string":"Institute of Mathematical and Computer Sciences (ICMC), University of S\u00e3o Paulo (USP), S\u00e3o Paulo, 13566-590, Brazil and Aschaffenburg University of Applied Sciences, Laboratory for Hybrid Modeling , Aschaffenburg 63743, Germany","institution_ids":["https://openalex.org/I4210158205","https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087625990","display_name":"Michael M\u00f6ckel","orcid":null},"institutions":[{"id":"https://openalex.org/I4210158205","display_name":"Aschaffenburg University of Applied Sciences","ror":"https://ror.org/04sms9203","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210158205"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael M\u00f6ckel","raw_affiliation_strings":["Aschaffenburg University of Applied Sciences, Laboratory for Hybrid Modeling , Aschaffenburg 63743, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aschaffenburg University of Applied Sciences, Laboratory for Hybrid Modeling , Aschaffenburg 63743, Germany","institution_ids":["https://openalex.org/I4210158205"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074464980","display_name":"Thaise G. L. de O. Toutain","orcid":"https://orcid.org/0000-0002-5201-0556"},"institutions":[{"id":"https://openalex.org/I126158947","display_name":"Universidade Federal da Bahia","ror":"https://ror.org/03k3p7647","country_code":"BR","type":"education","lineage":["https://openalex.org/I126158947"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Thaise G L de O Toutain","raw_affiliation_strings":["Health Sciences Institute(HSI), Federal University of Bahia (UFBA) , Bahia, 40110-909, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Health Sciences Institute(HSI), Federal University of Bahia (UFBA) , Bahia, 40110-909, Brazil","institution_ids":["https://openalex.org/I126158947"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090417570","display_name":"Joel Augusto Moura Porto","orcid":"https://orcid.org/0000-0002-0121-1403"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joel Augusto Moura Porto","raw_affiliation_strings":["Institute of Physics of S\u00e3o Carlos (IFSC), University of S\u00e3o Paulo (USP) , S\u00e3o Paulo, 13566-590, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Physics of S\u00e3o Carlos (IFSC), University of S\u00e3o Paulo (USP) , S\u00e3o Paulo, 13566-590, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061851539","display_name":"Francisco A. Rodrigues","orcid":"https://orcid.org/0000-0002-0145-5571"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Francisco A Rodrigues","raw_affiliation_strings":["Institute of Mathematical and Computer Sciences (ICMC), University of S\u00e3o Paulo (USP) , S\u00e3o Paulo, 13566-590, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Mathematical and Computer Sciences (ICMC), University of S\u00e3o Paulo (USP) , S\u00e3o Paulo, 13566-590, Brazil","institution_ids":["https://openalex.org/I17974374"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":4151,"currency":"USD","value_usd":4151},"apc_paid":null,"fwci":0.6531,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70834138,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"11","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.992900013923645,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6655387878417969},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.5860960483551025},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5843689441680908},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5368263125419617},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.42944830656051636},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4187335968017578},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.4158080518245697},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4087692201137543},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21280404925346375},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20870229601860046},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.09617769718170166}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6655387878417969},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.5860960483551025},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5843689441680908},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5368263125419617},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.42944830656051636},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4187335968017578},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4158080518245697},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4087692201137543},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21280404925346375},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20870229601860046},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.09617769718170166},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1093/comnet/cnad030","is_oa":false,"landing_page_url":"https://doi.org/10.1093/comnet/cnad030","pdf_url":null,"source":{"id":"https://openalex.org/S2531583244","display_name":"Journal of Complex Networks","issn_l":"2051-1310","issn":["2051-1310","2051-1329"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Complex Networks","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":88,"referenced_works":["https://openalex.org/W616176579","https://openalex.org/W1123205372","https://openalex.org/W1526741802","https://openalex.org/W1971421925","https://openalex.org/W1971937094","https://openalex.org/W1974764791","https://openalex.org/W2006052322","https://openalex.org/W2011521683","https://openalex.org/W2015953751","https://openalex.org/W2019150902","https://openalex.org/W2025543856","https://openalex.org/W2030035054","https://openalex.org/W2031968478","https://openalex.org/W2040956707","https://openalex.org/W2047940964","https://openalex.org/W2055026204","https://openalex.org/W2056944867","https://openalex.org/W2064675550","https://openalex.org/W2065604182","https://openalex.org/W2072596762","https://openalex.org/W2074466695","https://openalex.org/W2078197382","https://openalex.org/W2087194317","https://openalex.org/W2091613287","https://openalex.org/W2095705004","https://openalex.org/W2101267973","https://openalex.org/W2111722408","https://openalex.org/W2112090702","https://openalex.org/W2124073607","https://openalex.org/W2124209874","https://openalex.org/W2124637492","https://openalex.org/W2131681506","https://openalex.org/W2132202037","https://openalex.org/W2148606196","https://openalex.org/W2148606255","https://openalex.org/W2152702278","https://openalex.org/W2154642048","https://openalex.org/W2162800060","https://openalex.org/W2191319438","https://openalex.org/W2220576810","https://openalex.org/W2623825636","https://openalex.org/W2766619178","https://openalex.org/W2766736793","https://openalex.org/W2769133055","https://openalex.org/W2888171911","https://openalex.org/W2888456553","https://openalex.org/W2888518764","https://openalex.org/W2897943735","https://openalex.org/W2903973636","https://openalex.org/W2911964244","https://openalex.org/W2913418184","https://openalex.org/W2945976633","https://openalex.org/W2947814151","https://openalex.org/W2963028650","https://openalex.org/W2967899527","https://openalex.org/W2973049920","https://openalex.org/W2981521947","https://openalex.org/W3033865029","https://openalex.org/W3033926838","https://openalex.org/W3037114781","https://openalex.org/W3040734937","https://openalex.org/W3043310174","https://openalex.org/W3043553083","https://openalex.org/W3048817558","https://openalex.org/W3082260736","https://openalex.org/W3084378266","https://openalex.org/W3088930327","https://openalex.org/W3092000154","https://openalex.org/W3099768174","https://openalex.org/W3102799147","https://openalex.org/W3107766422","https://openalex.org/W3112426036","https://openalex.org/W3128328841","https://openalex.org/W3138498672","https://openalex.org/W3143569708","https://openalex.org/W3158097511","https://openalex.org/W3161546262","https://openalex.org/W3164147211","https://openalex.org/W3205824203","https://openalex.org/W4226408629","https://openalex.org/W4238452917","https://openalex.org/W4377022621","https://openalex.org/W6643463851","https://openalex.org/W6674330103","https://openalex.org/W6780673630","https://openalex.org/W6781875846","https://openalex.org/W6782082061","https://openalex.org/W6811483796"],"related_works":["https://openalex.org/W1488761988","https://openalex.org/W2044551864","https://openalex.org/W1572557500","https://openalex.org/W3124946120","https://openalex.org/W4390690393","https://openalex.org/W2047938026","https://openalex.org/W2585269888","https://openalex.org/W3132003399","https://openalex.org/W4293365552","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Abstract":[0],"Heart":[1],"disease,":[2,7],"also":[3],"known":[4],"as":[5,213],"cardiovascular":[6],"encompasses":[8],"a":[9,61,65,69,138,173],"variety":[10],"of":[11,123,181,191],"heart":[12,43,51],"conditions":[13],"that":[14,63,80,113],"can":[15,84,116,201],"result":[16],"in":[17,120,159],"sudden":[18],"death":[19],"for":[20,133],"many":[21],"people.":[22],"Examples":[23],"include":[24],"high":[25,108],"blood":[26],"pressure,":[27],"ischaemia,":[28],"irregular":[29],"heartbeats":[30],"and":[31,87,98,125,178],"pericardial":[32],"effusion.":[33],"Electrocardiogram":[34],"(ECG)":[35],"signal":[36],"analysis":[37],"is":[38,60],"frequently":[39],"used":[40],"to":[41,102,148,204,209],"diagnose":[42],"diseases,":[44,211],"providing":[45],"crucial":[46],"information":[47,182],"on":[48,72],"how":[49],"the":[50,73,81,95,104,121,129,134,142,155,186,192],"functions.":[52],"To":[53],"analyse":[54],"ECG":[55,122,189,206],"signals,":[56],"quantile":[57],"graphs":[58],"(QGs)":[59],"method":[62,97,115],"maps":[64],"time":[66],"series":[67],"into":[68],"network":[70],"based":[71],"time-series":[74],"fluctuation":[75],"proprieties.":[76],"Here,":[77],"we":[78,91,111,171],"demonstrate":[79],"QG":[82,96,156],"methodology":[83,200],"differentiate":[85],"younger":[86,196],"older":[88],"patients.":[89],"Furthermore,":[90,170],"construct":[92],"networks":[93,183,187],"from":[94,188],"use":[99],"machine-learning":[100],"algorithms":[101],"perform":[103],"automatic":[105],"diagnosis,":[106],"obtaining":[107],"accuracy.":[109],"Indeed,":[110],"verify":[112],"this":[114,199],"automatically":[117],"detect":[118],"changes":[119],"elderly":[124,193],"young":[126],"subjects,":[127],"with":[128,137,185,195],"highest":[130],"classification":[131],"performance":[132],"adjacency":[135],"matrix":[136],"mean":[139],"area":[140],"under":[141],"receiver":[143],"operating":[144],"characteristic":[145],"curve":[146],"close":[147],"one.":[149],"The":[150],"findings":[151],"reported":[152],"here":[153],"confirm":[154],"method\u2019s":[157],"utility":[158],"deciphering":[160],"intricate,":[161],"nonlinear":[162],"signals":[163],"like":[164],"those":[165],"produced":[166],"by":[167],"patient":[168],"ECGs.":[169],"find":[172],"more":[174,176],"significant,":[175],"connected":[177],"lower":[179],"distribution":[180],"associated":[184],"data":[190,207],"compared":[194],"subjects.":[197],"Finally,":[198],"be":[202],"applied":[203],"other":[205,210],"related":[208],"such":[212],"ischaemia.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
