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A Review of Methods and Applications for a Heart Rate Variability Analysis

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dc.abstract.enHeart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.
dc.affiliationWydział Nauk o Żywności i Żywieniu
dc.affiliation.instituteKatedra Mleczarstwa i Inżynierii Procesowej
dc.affiliation.instituteKatedra Fizyki i Biofizyki
dc.contributor.authorNayak, Suraj Kumar
dc.contributor.authorPradhan, Bikash
dc.contributor.authorMohanty, Biswaranjan
dc.contributor.authorSivaraman, Jayaraman
dc.contributor.authorRay, Sirsendu Sekhar
dc.contributor.authorWawrzyniak, Jolanta
dc.contributor.authorJarzębski, Maciej
dc.contributor.authorPal, Kunal
dc.date.access2025-05-29
dc.date.accessioned2025-09-01T08:21:51Z
dc.date.available2025-09-01T08:21:51Z
dc.date.copyright2023-09-09
dc.date.issued2023
dc.description.abstract<jats:p>Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if1,8
dc.description.number9
dc.description.points40
dc.description.versionfinal_published
dc.description.volume16
dc.identifier.doi10.3390/a16090433
dc.identifier.issn1999-4893
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4550
dc.identifier.weblinkhttp://www.mdpi.com/1999-4893/16/9/433
dc.languageen
dc.relation.ispartofAlgorithms
dc.relation.pagesart. 433
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.encannabis
dc.subject.enheart rate variability
dc.subject.ensignal analysis
dc.subject.enfeature selection
dc.subject.enartificial intelligence
dc.subtypeReviewArticle
dc.titleA Review of Methods and Applications for a Heart Rate Variability Analysis
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue9
oaire.citation.volume16