{"id":"https://openalex.org/W4390494043","doi":"https://doi.org/10.1109/icce-berlin58801.2023.10375625","title":"Markov Chain Modelling for Heart Rate Variability Analysis: Bridging Artificial Intelligence and Physiological Data","display_name":"Markov Chain Modelling for Heart Rate Variability Analysis: Bridging Artificial Intelligence and Physiological Data","publication_year":2023,"publication_date":"2023-09-03","ids":{"openalex":"https://openalex.org/W4390494043","doi":"https://doi.org/10.1109/icce-berlin58801.2023.10375625"},"language":"en","primary_location":{"id":"doi:10.1109/icce-berlin58801.2023.10375625","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icce-berlin58801.2023.10375625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin)","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/A5058861287","display_name":"Thi Thi Zin","orcid":null},"institutions":[{"id":"https://openalex.org/I118574687","display_name":"University of Miyazaki","ror":"https://ror.org/0447kww10","country_code":"JP","type":"education","lineage":["https://openalex.org/I118574687"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Thi Thi Zin","raw_affiliation_strings":["Graduate School of Engineering, University of Miyazaki,Miyazaki,Japan","Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, University of Miyazaki,Miyazaki,Japan","institution_ids":["https://openalex.org/I118574687"]},{"raw_affiliation_string":"Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan","institution_ids":["https://openalex.org/I118574687"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023940062","display_name":"Pyke Tin","orcid":"https://orcid.org/0000-0002-3623-2984"},"institutions":[{"id":"https://openalex.org/I118574687","display_name":"University of Miyazaki","ror":"https://ror.org/0447kww10","country_code":"JP","type":"education","lineage":["https://openalex.org/I118574687"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Pyke Tin","raw_affiliation_strings":["Graduate School of Engineering, University of Miyazaki,Miyazaki,Japan","Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, University of Miyazaki,Miyazaki,Japan","institution_ids":["https://openalex.org/I118574687"]},{"raw_affiliation_string":"Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan","institution_ids":["https://openalex.org/I118574687"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5058861287"],"corresponding_institution_ids":["https://openalex.org/I118574687"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39414294,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"163","last_page":"166"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9995999932289124,"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9995999932289124,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.97079998254776,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.7719865441322327},{"id":"https://openalex.org/keywords/heart-rate-variability","display_name":"Heart rate variability","score":0.7708520889282227},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.634560763835907},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5980277061462402},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5182404518127441},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5084477663040161},{"id":"https://openalex.org/keywords/maximum-entropy-markov-model","display_name":"Maximum-entropy Markov model","score":0.4956158399581909},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.47693556547164917},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.47484949231147766},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4016619324684143},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34712398052215576},{"id":"https://openalex.org/keywords/heart-rate","display_name":"Heart rate","score":0.17661291360855103}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7719865441322327},{"id":"https://openalex.org/C71635504","wikidata":"https://www.wikidata.org/wiki/Q933954","display_name":"Heart rate variability","level":4,"score":0.7708520889282227},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.634560763835907},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5980277061462402},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5182404518127441},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5084477663040161},{"id":"https://openalex.org/C196956702","wikidata":"https://www.wikidata.org/wiki/Q6795829","display_name":"Maximum-entropy Markov model","level":5,"score":0.4956158399581909},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.47693556547164917},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.47484949231147766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4016619324684143},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34712398052215576},{"id":"https://openalex.org/C2777953023","wikidata":"https://www.wikidata.org/wiki/Q1073121","display_name":"Heart rate","level":3,"score":0.17661291360855103},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-berlin58801.2023.10375625","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icce-berlin58801.2023.10375625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2024504058","https://openalex.org/W2051620101","https://openalex.org/W2111772099","https://openalex.org/W2162800060","https://openalex.org/W2169502373","https://openalex.org/W2800521154","https://openalex.org/W2888483834","https://openalex.org/W2980106875","https://openalex.org/W2993420974","https://openalex.org/W3112983769","https://openalex.org/W3191360768","https://openalex.org/W4221134483","https://openalex.org/W4363673194"],"related_works":["https://openalex.org/W2379938888","https://openalex.org/W2382132287","https://openalex.org/W2130519334","https://openalex.org/W2134386692","https://openalex.org/W2116722627","https://openalex.org/W1977445474","https://openalex.org/W1510894296","https://openalex.org/W2799426416","https://openalex.org/W2194396582","https://openalex.org/W2566202039"],"abstract_inverted_index":{"Heart":[0],"rate":[1,147],"variability":[2],"(HRV)":[3],"is":[4,16],"a":[5,17,64,101,193],"vital":[6],"measure":[7,141],"that":[8],"captures":[9],"the":[10,48,81,111,116,122,129,138,152,160,163,170,174,186,198],"variations":[11],"in":[12,181],"beat-to-beat":[13,98],"intervals.":[14],"It":[15],"significant":[18],"indicator":[19],"of":[20,50,54,83,103,154,162,173,176,189,201],"autonomic":[21],"nervous":[22],"system":[23],"activity":[24],"and":[25,58,69,127,179,203],"has":[26],"been":[27],"linked":[28],"to":[29,47,87,142,158],"various":[30],"health":[31],"conditions.":[32],"Analyzing":[33],"HRV":[34,88,183,202],"involves":[35],"processing":[36],"complex":[37],"physiological":[38],"data":[39,157],"with":[40,73],"inherent":[41],"uncertainties.":[42],"Artificial":[43],"Intelligence":[44],"(AI)":[45],"refers":[46],"development":[49],"intelligent":[51],"machines":[52],"capable":[53],"mimicking":[55],"human":[56,207],"intelligence":[57],"learning.":[59],"Markov":[60,84,107,177],"Chain":[61,85,108,178],"theory":[62],"provides":[63],"powerful":[65],"framework":[66],"for":[67,206],"studying":[68],"mathematically":[70],"modelling":[71],"situations":[72],"random":[74,104],"variables.":[75],"In":[76],"this":[77],"paper,":[78],"we":[79,136,150],"explore":[80],"application":[82],"concepts":[86],"analysis,":[89],"treating":[90],"it":[91],"as":[92,100],"an":[93],"AI":[94,180],"problem.":[95],"We":[96,120],"represent":[97],"intervals":[99],"sequence":[102],"variables,":[105],"forming":[106],"states":[109],"where":[110],"current":[112],"state":[113],"depends":[114],"on":[115,196],"immediate":[117],"previous":[118],"state.":[119],"derive":[121],"state-to-state":[123],"probability":[124],"transition":[125],"matrix":[126],"compute":[128],"stationary":[130],"distribution":[131],"probabilities.":[132],"Using":[133],"these":[134],"probabilities,":[135],"calculate":[137],"Shannon":[139],"entropy":[140],"gain":[143],"insights":[144],"into":[145],"heart":[146],"variability.":[148],"Furthermore,":[149],"present":[151],"utilization":[153],"real-life":[155],"experimental":[156,167],"illustrate":[159],"effectiveness":[161],"proposed":[164],"method.":[165],"The":[166],"results":[168],"demonstrate":[169],"promising":[171],"potential":[172],"integration":[175],"analyzing":[182],"by":[184],"achieving":[185],"highest":[187],"accuracy":[188],"95%.":[190],"This":[191],"research":[192],"novel":[194],"perspective":[195],"understanding":[197],"underlying":[199],"dynamics":[200],"its":[204],"implications":[205],"health.":[208]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}