{"id":"https://openalex.org/W7083799119","doi":"https://doi.org/10.48550/arxiv.2509.23317","title":"Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery","display_name":"Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery","publication_year":2025,"publication_date":"2025-09-27","ids":{"openalex":"https://openalex.org/W7083799119","doi":"https://doi.org/10.48550/arxiv.2509.23317"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2509.23317","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.23317","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2509.23317","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Maluckov, A.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maluckov, A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Stojanovic, D.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stojanovic, D.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Miletic, M.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miletic, M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hadzievski, Lj.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hadzievski, Lj.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Petrovic, J.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Petrovic, J.","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6662999987602234,"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"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6662999987602234,"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"}},{"id":"https://openalex.org/T13067","display_name":"Geological Modeling and Analysis","score":0.023800000548362732,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.020099999383091927,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multifractal-system","display_name":"Multifractal system","score":0.9164000153541565},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5627999901771545},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5408999919891357},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.491100013256073},{"id":"https://openalex.org/keywords/singularity","display_name":"Singularity","score":0.429500013589859},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.41679999232292175},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.40529999136924744},{"id":"https://openalex.org/keywords/fractal","display_name":"Fractal","score":0.4052000045776367}],"concepts":[{"id":"https://openalex.org/C133905733","wikidata":"https://www.wikidata.org/wiki/Q2629238","display_name":"Multifractal system","level":3,"score":0.9164000153541565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.602400004863739},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5627999901771545},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.491100013256073},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43689998984336853},{"id":"https://openalex.org/C16171025","wikidata":"https://www.wikidata.org/wiki/Q863349","display_name":"Singularity","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C40636538","wikidata":"https://www.wikidata.org/wiki/Q81392","display_name":"Fractal","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40049999952316284},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3407000005245209},{"id":"https://openalex.org/C2983030100","wikidata":"https://www.wikidata.org/wiki/Q638328","display_name":"Nonlinear dynamical systems","level":3,"score":0.31299999356269836},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C43456602","wikidata":"https://www.wikidata.org/wiki/Q7303254","display_name":"Recurrence quantification analysis","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C142806159","wikidata":"https://www.wikidata.org/wiki/Q7646876","display_name":"Surrogate data","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2509.23317","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.23317","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2509.23317","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.23317","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"investigate":[1],"the":[2,23,27],"recovery":[3,63,85],"dynamics":[4],"of":[5,30],"healthy":[6],"cardiac":[7],"activity":[8],"after":[9],"physical":[10],"exertion":[11],"using":[12],"multimodal":[13,78],"biosignals":[14],"recorded":[15],"with":[16,44,77],"a":[17,66],"polycardiograph.":[18],"Multifractal":[19],"features":[20,82],"derived":[21],"from":[22],"singularity":[24],"spectrum":[25],"capture":[26],"scale-invariant":[28],"properties":[29],"cardiovascular":[31],"regulation.":[32],"Five":[33],"supervised":[34],"classification":[35],"algorithms":[36],"-":[37,58],"Logistic":[38],"Regression":[39],"(LogReg),":[40],"Suport":[41],"Vector":[42],"Machine":[43],"RBF":[45],"kernel":[46],"(SVM-RBF),":[47],"k-Nearest":[48],"Neighbors":[49],"(kNN),":[50],"Decision":[51],"Tree":[52],"(DT),":[53],"and":[54,86],"Random":[55],"Forest":[56],"(RF)":[57],"were":[59],"evaluated":[60],"to":[61],"distinguish":[62],"states":[64],"in":[65],"small,":[67],"imbalanced":[68],"dataset.":[69],"Our":[70],"results":[71],"show":[72],"that":[73],"multifractal":[74],"analysis,":[75],"combined":[76],"sensing,":[79],"yields":[80],"reliable":[81],"for":[83,92],"characterizing":[84],"points":[87],"toward":[88],"nonlinear":[89],"diagnostic":[90],"methods":[91],"heart":[93],"conditions.":[94]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
