{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T21:31:51Z","timestamp":1783114311469,"version":"3.54.6"},"reference-count":115,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:p>Heart rate variability (HRV) entropy analysis is an emerging tool for assessing autonomic function, particularly in older and diabetic populations. Traditional HRV metrics, limited in capturing signal complexity, have been supplemented by advanced entropy methods such as multi-scale entropy (MSE), permutation entropy (PermEn), and the baroreflex entropy index (BEI). In this review, we introduce novel entropy indices, including percussion entropy (PercEn), and explore their potential to enhance HRV assessment. Additionally, we discuss the integration of novel data sources, such as pulse wave velocity (PWV) and crest time, offering an in-depth evaluation of autonomic dysfunction. Performance metrics such as classification accuracy (up to 92.5% in diabetic autonomic dysfunction prediction), sensitivity (87.3%), and specificity (89.1%) demonstrate the potential clinical utility of entropy-based HRV analysis. The key challenges include the prognostic value of entropy metrics, the impact of confounding factors, and the need for standardised methodologies. Advances in machine learning and wearable technology are also examined for real-time HRV monitoring and personalised healthcare. The findings highlight entropy analysis as a promising avenue for autonomic dysfunction assessment, with future research needed to optimise methodologies and establish clinical validation.<\/jats:p>","DOI":"10.3389\/fphys.2026.1795118","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T22:10:30Z","timestamp":1775772630000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Advancing entropy analysis for heart rate variability: clinical insights for aging and diabetes"],"prefix":"10.3389","volume":"17","author":[{"given":"Shanglin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, North Minzu University","place":["Yinchuan, Ningxia, China"]},{"name":"Microelectronics and Solid-State Electronics Device Research Centre, North Minzu University","place":["Yinchuan, Ningxia, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsientsai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, North Minzu University","place":["Yinchuan, Ningxia, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuwei","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, North Minzu University","place":["Yinchuan, Ningxia, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyang","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, North Minzu University","place":["Yinchuan, Ningxia, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juin J.","family":"Liou","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, North Minzu University","place":["Yinchuan, Ningxia, China"]},{"name":"Microelectronics and Solid-State Electronics Device Research Centre, North Minzu University","place":["Yinchuan, Ningxia, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1142\/s0219519417400085","article-title":"The effect of diabetes on cardiovascular system","volume":"17","author":"Adam","year":"2017","journal-title":"J. Mech. Med. Biol."},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12185952","article-title":"Association between the prognostic nutritional index and chronic microvascular complications in patients with type 2 diabetes mellitus","volume":"12","author":"Aktas","year":"2023","journal-title":"J. Clin. Med."},{"key":"B3","doi-asserted-by":"publisher","first-page":"104206","DOI":"10.20944\/preprints202309.0714.v1","article-title":"Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches","volume":"130","author":"Al-Qazzaz","year":"2024","journal-title":"Med. Eng. Phys."},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3390\/s22228903","article-title":"Wearable devices for remote monitoring of heart rate and heart rate variability\u2014What we know and what is coming","volume":"22","author":"Alugubelli","year":"2022","journal-title":"Sensors"},{"key":"B5","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.2337\/diacare.27.2007.s15","article-title":"Standards of medical care in diabetes","volume":"46","year":"2023","journal-title":"Diabetes Care"},{"key":"B6","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1097\/wnp.0000000000000625","article-title":"Tilt test: a review","volume":"38","author":"Aponte-Becerra","year":"2021","journal-title":"J. Clin. Neurophysiol."},{"key":"B7","doi-asserted-by":"publisher","first-page":"051001","DOI":"10.1088\/1741-2552\/acf8fa","article-title":"Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer\u2019s and Parkinson\u2019s disease","volume":"20","author":"Averna","year":"2023","journal-title":"J. Neural Eng."},{"key":"B8","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3934\/medsci.2024004","article-title":"The relationship of vitamin D levels with hemogram indices and metabolic parameters in patients with type 2 diabetes mellitus","volume":"11","author":"Basaran","year":"2024","journal-title":"AIMS Med. Sci."},{"key":"B9","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1038\/s41569-021-00522-7","article-title":"Smart wearable devices in cardiovascular care: where we are and how to move forward","volume":"18","author":"Bayoumy","year":"2021","journal-title":"Nat. Rev. Cardiol."},{"key":"B10","doi-asserted-by":"publisher","first-page":"e56","DOI":"10.1161\/CIR.0000000000000659","article-title":"Heart disease and stroke statistics-2019 update: A report from the American Heart Association","volume":"139","author":"Benjamin","year":"2019","journal-title":"Circulation"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.85.021906","article-title":"Modified permutation-entropy analysis of heartbeat dynamics","volume":"85","author":"Bian","year":"2012","journal-title":"Phys. Rev. E Stat. Nonlin Soft Matter Phys."},{"key":"B12","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1038\/s41598-022-06301-9","article-title":"Early heart rate variability evaluation enables to predict ICU patients\u2019 outcome","volume":"12","author":"Bodenes","year":"2022","journal-title":"Sci. Rep."},{"key":"B13","doi-asserted-by":"publisher","first-page":"156","DOI":"10.2174\/1573399817666210412123740","article-title":"Diagnostic tools, biomarkers, and treatments in diabetic polyneuropathy and cardiovascular autonomic neuropathy","volume":"18","author":"B\u00f6nhof","year":"2022","journal-title":"Curr. Diabetes Rev."},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2022.937701","article-title":"Heart rate variability for the early detection of cardiac autonomic dysfunction in type 1 diabetes","volume":"13","author":"Castiglioni","year":"2022","journal-title":"Front. Physiol."},{"key":"B15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.37256\/aie.3120221252","article-title":"Statistical validation of cardiovascular digital biomarkers towards monitoring the cardiac risk in COPD: A lyfas case study","volume":"3","author":"Chattopadhyay","year":"2022","journal-title":"Artif. Intell. Evol."},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11101918","article-title":"Reactive hyperaemia-triggered wrist pulse analysis for early monitoring of young men with high atherosclerotic risk","volume":"11","author":"Chen","year":"2021","journal-title":"Diagnostics"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1042\/CS20190254","article-title":"Prenatal cold exposure causes hypertension in offspring by hyperactivity of the sympathetic nervous system","volume":"133","author":"Chen","year":"2019","journal-title":"Clin. Sci. (Lond)"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2023.1157270","article-title":"Heart rate variability in different sleep stages is associated with metabolic function and glycaemic control in type 2 diabetes mellitus","volume":"14","author":"Cheng","year":"2023","journal-title":"Front. Physiol."},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0147282","article-title":"Detrended fluctuation analysis of heart rate dynamics is an important prognostic factor in patients with end-stage renal disease receiving peritoneal dialysis","volume":"11","author":"Chiang","year":"2016","journal-title":"PLoS One"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2014.00034","article-title":"How to calculate Renyi entropy from heart rate variability, and why it matters for detecting cardiac autonomic neuropathy","volume":"2","author":"Cornforth","year":"2014","journal-title":"Front. Bioeng. Biotechnol."},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.3390\/app13137824","article-title":"The use of empirical mode decomposition on heart rate variability signals to assess autonomic neuropathy progression in type 2 diabetes","volume":"13","author":"Cossul","year":"2023","journal-title":"Appl. Sci."},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.89.068102","article-title":"Multiscale entropy analysis of complex physiologic time series","volume":"89","author":"Costa","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.71.021906","article-title":"Multiscale entropy analysis of biological signals","volume":"71","author":"Costa","year":"2005","journal-title":"Phys. Rev. E Stat. Nonlin Soft Matter Phys."},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.2196\/28901","article-title":"The potential of current non-invasive wearable technology for the monitoring of physiological signals in the management of type 1 diabetes: literature survey","volume":"24","author":"Daskalaki","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.3390\/e22030317","article-title":"Entropy in heart rate dynamics reflects how HRV-biofeedback training improves neurovisceral complexity during stress-cognition interactions","volume":"22","author":"Deschodt-Arsac","year":"2020","journal-title":"Entropy"},{"key":"B26","unstructured":"DF\n            \n          \n          Diabetes Atlas 2022 reports"},{"key":"B27","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/rbme.2020.2992838","article-title":"Wearable sensing and telehealth technology with potential applications in the coronavirus pandemic","volume":"14","author":"Ding","year":"2020","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"B28","doi-asserted-by":"publisher","first-page":"103064","DOI":"10.3233\/978-1-60750-497-9-162","article-title":"Characterization and classification of Parkinson\u2019s disease patients based on symbolic dynamics analysis of heart rate variability","volume":"71","author":"Dorantes-M\u00e9ndez","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"B29","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1038\/s41746-019-0136-7","article-title":"The use of photoplethysmography for assessing hypertension","volume":"2","author":"Elgendi","year":"2019","journal-title":"NPJ Digit Med."},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3390\/sym14061139","article-title":"Peak detection and HRV feature evaluation on ECG and PPG signals","volume":"14","author":"Esgalhado","year":"2022","journal-title":"Symmetry"},{"key":"B31","doi-asserted-by":"publisher","first-page":"123114","DOI":"10.1063\/1.5115506","article-title":"Comparison of methods for the assessment of nonlinearity in short-term heart rate variability under different physiopathological states","volume":"29","author":"Faes","year":"2019","journal-title":"Chaos"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-019-0092-1","article-title":"Diabetic neuropathy","volume":"5","author":"Feldman","year":"2019","journal-title":"Nat. Rev. Dis. Primers"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering8090122","article-title":"Multiscale entropy analysis of heart rate variability in neonatal patients with and without seizures","volume":"8","author":"Frassineti","year":"2021","journal-title":"Bioengineering"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.2337\/diacare.24.10.1793","article-title":"Impaired autonomic function is associated with increased mortality, especially in subjects with diabetes, hypertension, or a history of cardiovascular disease: the Hoorn Study","volume":"24","author":"Gerritsen","year":"2001","journal-title":"Diabetes Care"},{"key":"B35","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s11416-018-0323-0","article-title":"Using convolutional neural networks for classification of malware represented as images","volume":"15","author":"Gibert","year":"2019","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"B36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/lsens.2022.3203609","article-title":"DSVRI: a PPG-based novel feature for early diagnosis of type-II diabetes mellitus","volume":"6","author":"Gupta","year":"2022","journal-title":"IEEE Sensors Lett."},{"key":"B37","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.cmpb.2018.10.001","article-title":"Application of multiscale Poincar\u00e9 short-time computation versus multiscale entropy in analysing fingertip photoplethysmogram amplitudes to differentiate diabetic from non-diabetic subjects","volume":"166","author":"Haryadi","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0018699","article-title":"The prognostic value of non-linear analysis of heart rate variability in patients with congestive heart failure\u2014a pilot study of multiscale entropy","volume":"6","author":"Ho","year":"2011","journal-title":"PLoS One"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105424","article-title":"A unified technique for entropy enhancement based diabetic retinopathy detection using hybrid neural network","volume":"145","author":"Imran","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.3390\/e22010045","article-title":"(Multiscale) cross-entropy methods: A review","volume":"22","author":"Jamin","year":"2019","journal-title":"Entropy (Basel)"},{"key":"B41","doi-asserted-by":"publisher","first-page":"e77401","DOI":"10.7759\/cureus.77401","article-title":"Heart rate variability and peripheral oxygen saturation levels in chronic obstructive pulmonary disease","volume":"17","author":"Jeena","year":"2025","journal-title":"Cureus"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2020.607324","article-title":"Diffusion entropy vs. multiscale and Renyi entropy to detect progression of autonomic neuropathy","volume":"11","author":"Jelinek","year":"2021","journal-title":"Front. Physiol."},{"key":"B43","doi-asserted-by":"publisher","first-page":"5229","DOI":"10.1007\/s11277-017-4777-z","article-title":"The PPG physiological signal for heart rate variability analysis","volume":"97","author":"Jong","year":"2017","journal-title":"Wireless Pers. Commun."},{"key":"B44","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1038\/s41746-021-00515-3","article-title":"Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine","volume":"4","author":"Kang","year":"2021","journal-title":"NPJ Digital Med."},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.3390\/e22121396","article-title":"Entropy profiling: A reduced\u2014parametric measure of Kolmogorov\u2014Sinai entropy from short-term HRV signal","volume":"22","author":"Karmakar","year":"2020","journal-title":"Entropy"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.3390\/e26010021","article-title":"Quantifying the resilience of a healthcare system: Entropy and network science perspectives","volume":"26","author":"Klemann","year":"2024","journal-title":"Entropy"},{"key":"B47","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.biopsycho.2010.03.010","article-title":"Autonomic nervous system activity in emotion: A review","volume":"84","author":"Kreibig","year":"2010","journal-title":"Biol. Psychol."},{"key":"B48","volume-title":"Photoplethysmography: Technology, signal analysis and applications","author":"Kyriacou","year":"2021"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.3390\/s20051493","article-title":"Motion artifact reduction in wearable photoplethysmography based on multi-channel sensors with multiple wavelengths","volume":"20","author":"Lee","year":"2020","journal-title":"Sensors"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1177\/1177271918786931","article-title":"Heart rate variability as a biomarker for predicting stroke, post-stroke complications and functionality","volume":"13","author":"Lees","year":"2018","journal-title":"biomark. Insights"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph20247146","article-title":"Heart rate variability measurement through a smart wearable device: Another breakthrough for personal health monitoring","volume":"20","author":"Li","year":"2023","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2023.1196750","article-title":"Evaluation of autonomic nervous system activity in intradialytic hypotension using entropy and skin sympathetic nerve activity","volume":"17","author":"Li","year":"2023","journal-title":"Front. Neurosci."},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.3390\/e23030267","article-title":"Short-term HRV analysis using nonparametric sample entropy for obstructive sleep apnoea","volume":"23","author":"Liang","year":"2021","journal-title":"Entropy"},{"key":"B54","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s12938-022-00999-4","article-title":"The autonomic balance of heart rhythm complexity after renal artery denervation: insight from entropy of entropy and average entropy analysis","volume":"21","author":"Lin","year":"2022","journal-title":"Biomed. Eng. Online"},{"key":"B55","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.compbiomed.2012.11.005","article-title":"Analysis of heart rate variability using fuzzy measure entropy","volume":"43","author":"Liu","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1038\/srep44363","article-title":"Cardiac autonomic alteration and metabolic syndrome: an ambulatory ECG-based study in a general population","volume":"7","author":"Ma","year":"2017","journal-title":"Sci. Rep."},{"key":"B57","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1161\/circresaha.114.302524","article-title":"The autonomic nervous system and hypertension","volume":"114","author":"Mancia","year":"2014","journal-title":"Circ. Res."},{"key":"B58","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/TII.2020.3007152","article-title":"Deep recurrent entropy adaptive model for system reliability monitoring","volume":"17","author":"Mart\u00ednez-Garc\u00eda","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"key":"B59","doi-asserted-by":"publisher","first-page":"20023","DOI":"10.1038\/s41598-022-24415-y","article-title":"Generalisable machine learning models trained on heart rate variability data to predict mental fatigue","volume":"12","author":"Matuz","year":"2022","journal-title":"Sci. Rep."},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.3390\/e25020301","article-title":"Complexity and entropy in physiological signals (ceps): Resonance breathing rate assessed using measures of fractal dimension, heart rate asymmetry and permutation entropy","volume":"25","author":"Mayor","year":"2023","journal-title":"Entropy"},{"key":"B61","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1002\/mdc3.14029","article-title":"Systemic symptoms in Huntington's disease: A comprehensive review","volume":"11","author":"Mehanna","year":"2024","journal-title":"Mov. Disord. Clin. Pract."},{"key":"B62","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/s42600-023-00287-7","article-title":"Type 2 diabetes classification from short photoplethysmogram signal using multiple domain features and machine learning techniques","volume":"39","author":"Mishra","year":"2023","journal-title":"Res. Biomed. Eng."},{"key":"B63","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1038\/hr.2010.126","article-title":"Brachial-ankle pulse wave velocity as a risk stratification index for the short-term prognosis of type 2 diabetic patients with coronary artery disease","volume":"33","author":"Nakamura","year":"2010","journal-title":"Hypertens. Res."},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.10178","article-title":"Assessment of autonomous nerve system through non-linear heart rate variability outcomes in sedentary healthy adults","volume":"8","author":"Navarro-Lomas","year":"2020","journal-title":"PeerJ"},{"key":"B65","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1016\/0735-1097(94)90379-4","article-title":"Effect of beta-blockade on heart rate variability in patients with coronary artery disease","volume":"23","author":"Niemel\u00e4","year":"1994","journal-title":"J. Am. Coll. Cardiol."},{"key":"B66","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3390\/e17010231","article-title":"Multiscale entropy analysis of heart rate variability for assessing the severity of sleep disordered breathing","volume":"17","author":"Pan","year":"2015","journal-title":"Entropy"},{"key":"B67","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.jcrc.2007.04.006","article-title":"Investigation of heart rate and blood pressure variability, baroreflex sensitivity, and approximate entropy in acute brain injury patients","volume":"23","author":"Papaioannou","year":"2008","journal-title":"J. Crit. Care"},{"key":"B68","doi-asserted-by":"publisher","DOI":"10.3390\/s21123998","article-title":"Heart rate variability in psychology: A review of HRV indices and an analysis tutorial","volume":"21","author":"Pham","year":"2021","journal-title":"Sensors"},{"key":"B69","doi-asserted-by":"publisher","DOI":"10.31083\/j.rcm2507266","article-title":"Pulse wave velocity: Methodology, clinical applications, and interplay with heart rate variability","volume":"25","author":"Pilz","year":"2024","journal-title":"Rev. Cardiovasc. Med."},{"key":"B70","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1177\/0268355516678513","article-title":"Valsalva manoeuvre in phlebologic practice","volume":"33","author":"Ricci","year":"2018","journal-title":"Phlebology"},{"key":"B71","doi-asserted-by":"publisher","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.h2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"B72","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2018.00769","article-title":"A heartbeat away from consciousness: heart rate variability entropy can discriminate disorders of consciousness and is correlated with resting-state fMRI brain connectivity of the central autonomic network","volume":"9","author":"Riganello","year":"2018","journal-title":"Front. Neurol."},{"key":"B73","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jelectrocard.2024.03.006","article-title":"Which indices of heart rate variability are the best predictors of mortality after acute myocardial infarction? Meta-analysis of observational studies","volume":"84","author":"Rueda-Ochoa","year":"2024","journal-title":"J. Electrocardiol."},{"key":"B74","doi-asserted-by":"publisher","first-page":"23274","DOI":"10.1038\/s41598-024-74357-w","article-title":"Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model","volume":"14","author":"Rustam","year":"2024","journal-title":"Sci. Rep."},{"key":"B75","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2019.00116","article-title":"The application of the extended Poincar\u00e9 plot in the analysis of physiological variabilities","volume":"10","author":"Satti","year":"2019","journal-title":"Front. Physiol."},{"key":"B76","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.2337\/diacare.28.8.2013","article-title":"Identifying individuals at high risk for diabetes: The Atherosclerosis Risk in Communities study","volume":"28","author":"Schmidt","year":"2005","journal-title":"Diabetes Care"},{"key":"B77","doi-asserted-by":"publisher","first-page":"107441","DOI":"10.1016\/j.compbiomed.2023.107441","article-title":"Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013\u20132023)","volume":"165","author":"Seoni","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"B78","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2017.00258","article-title":"An overview of heart rate variability metrics and norms","volume":"5","author":"Shaffer","year":"2017","journal-title":"Front. Public Health"},{"key":"B79","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.autrev.2017.11.010","article-title":"Physical activity and autoimmune diseases: Get moving and manage the disease","volume":"17","author":"Sharif","year":"2018","journal-title":"Autoimmun. Rev."},{"key":"B80","doi-asserted-by":"publisher","first-page":"23817","DOI":"10.1038\/s41598-021-03441-2","article-title":"Efficient detection of aortic stenosis using morphological characteristics of cardiomechanical signals and heart rate variability parameters","volume":"11","author":"Shokouhmand","year":"2021","journal-title":"Sci. Rep."},{"key":"B81","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3322\/caac.21442","article-title":"Cancer statistics","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J. Clin."},{"key":"B82","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/tbme.2018.2834546","article-title":"Biomarkers for Alzheimer's disease defined by a novel brain functional network measure","volume":"66","author":"Song","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B83","doi-asserted-by":"publisher","first-page":"3","DOI":"10.4093\/dmj.2018.0259","article-title":"Update on the impact, diagnosis and management of cardiovascular autonomic neuropathy in diabetes: what is defined, what is new, and what is unmet","volume":"43","author":"Spallone","year":"2019","journal-title":"Diabetes Metab. J."},{"key":"B84","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1002\/dmrr.1239","article-title":"Cardiovascular autonomic neuropathy in diabetes: clinical impact, assessment, diagnosis, and management","volume":"27","author":"Spallone","year":"2011","journal-title":"Diabetes Metab. Res. Rev."},{"key":"B85","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph191610368","article-title":"Arterial stiffness assessment by pulse wave velocity in patients with metabolic syndrome and its components: Is it a useful tool in clinical practice","volume":"19","author":"Starzak","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"B86","doi-asserted-by":"publisher","DOI":"10.3389\/fspor.2021.585870","article-title":"Assessing the accuracy of popular commercial technologies that measure resting heart rate and heart rate variability","volume":"3","author":"Stone","year":"2021","journal-title":"Front. Sports Active Living"},{"key":"B87","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.jpain.2023.08.002","article-title":"Higher cardiovagal baroreflex sensitivity predicts increased pain outcomes after cardiothoracic surgery","volume":"25","author":"Suarez-Roca","year":"2024","journal-title":"J. Pain"},{"key":"B88","volume-title":"Textbook of physical diagnosis E-book: history and examination","author":"Swartz","year":"2014"},{"key":"B89","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1159\/000495519","article-title":"Validity of commonly used heart rate variability markers of autonomic nervous system function","volume":"78","author":"Thomas","year":"2019","journal-title":"Neuropsychobiology"},{"key":"B90","doi-asserted-by":"publisher","first-page":"e160721189770","DOI":"10.2174\/1573403x16999201231203854","article-title":"Analysis of heart rate variability and implication of different factors on heart rate variability","volume":"17","author":"Tiwari","year":"2021","journal-title":"Curr. Cardiol. Rev."},{"key":"B91","doi-asserted-by":"crossref","first-page":"e93","DOI":"10.1161\/CIR.0000000000001123","article-title":"Heart disease and stroke statistics-2023 update: A report from the American Heart Association","volume":"147","author":"Tsao","year":"2023","journal-title":"Circulation"},{"key":"B92","doi-asserted-by":"publisher","first-page":"2202","DOI":"10.1109\/tbme.2009.2021986","article-title":"Refined multiscale entropy: Application to 24-h holter recordings of heart period variability in healthy and aortic stenosis subjects","volume":"56","author":"Valencia","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B93","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1111\/jdi.12042","article-title":"Diabetic cardiac autonomic neuropathy, inflammation and cardiovascular disease","volume":"4","author":"Vinik","year":"2013","journal-title":"J. Diabetes Investig."},{"key":"B94","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.bspc.2018.01.001","article-title":"Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects","volume":"42","author":"Wang","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"B95","doi-asserted-by":"publisher","DOI":"10.3390\/e22070754","article-title":"Prognosis of diabetic peripheral neuropathy via decomposed digital volume pulse from the fingertip","volume":"22","author":"Wei","year":"2020","journal-title":"Entropy (Basel)"},{"key":"B96","doi-asserted-by":"publisher","DOI":"10.3390\/e21121229","article-title":"Digital volume pulse measured at the fingertip as an indicator of diabetic peripheral neuropathy in the aged and diabetic","volume":"21","author":"Wei","year":"2019","journal-title":"Entropy"},{"key":"B97","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics10010032","article-title":"Percussion entropy analysis of synchronized ECG and PPG signals as a prognostic indicator for future peripheral neuropathy in type 2 diabetic subjects","volume":"10","author":"Wei","year":"2020","journal-title":"Diagnostics"},{"key":"B98","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6469853","article-title":"Assessment of diabetic autonomic nervous dysfunction with a novel percussion entropy approach","volume":"2019","author":"Wei","year":"2019","journal-title":"Complexity"},{"key":"B99","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1001\/jama.2009.460","article-title":"Hypoglycaemic episodes and risk of dementia in older patients with type 2 diabetes mellitus","volume":"301","author":"Whitmer","year":"2009","journal-title":"JAMA"},{"key":"B100","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106124","article-title":"Multiscale entropy with electrocardiograph, electromyography, electroencephalography, and photoplethysmography signals in healthcare: A twelve-year systematic review","volume":"93","author":"Wu","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"B101","doi-asserted-by":"publisher","first-page":"373","DOI":"10.37290\/ctnr2641-452x.21:373-379","article-title":"Skin carotenoid score as an important prognostic factor for predicting type 2 diabetes mellitus in middle-aged subjects","volume":"21","author":"Wu","year":"2023","journal-title":"Curr. Top. Nutraceutical Res."},{"key":"B102","doi-asserted-by":"publisher","first-page":"2978","DOI":"10.1109\/tbme.2011.2159975","article-title":"Multiscale entropy analysis of pulse wave velocity for assessing atherosclerosis in the aged and diabetic","volume":"58","author":"Wu","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B103","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jtbi.2013.03.022","article-title":"Assessment of autonomic dysfunction in patients with type 2 diabetes using reactive hyperaemia","volume":"330","author":"Wu","year":"2013","journal-title":"J. Theor. Biol."},{"key":"B104","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.atherosclerosis.2010.08.075","article-title":"Novel application of parameters in waveform contour analysis for assessing arterial stiffness in aged and atherosclerotic subjects","volume":"213","author":"Wu","year":"2010","journal-title":"Atherosclerosis"},{"key":"B105","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jneumeth.2015.01.015","article-title":"The detection of epileptic seizure signals based on fuzzy entropy","volume":"243","author":"Xiang","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"B106","doi-asserted-by":"publisher","DOI":"10.3390\/e21090894","article-title":"Application of a speedy modified entropy method in assessing the complexity of baroreflex sensitivity for age-controlled healthy and diabetic subjects","volume":"21","author":"Xiao","year":"2019","journal-title":"Entropy"},{"key":"B107","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.bbe.2021.08.001","article-title":"Machine learning prediction of future peripheral neuropathy in type 2 diabetics with percussion entropy and body mass indices","volume":"41","author":"Xiao","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"B108","doi-asserted-by":"publisher","DOI":"10.3390\/e25121582","article-title":"Analysis of the chaotic component of photoplethysmography and its association with hemodynamic parameters","volume":"25","author":"Xing","year":"2023","journal-title":"Entropy"},{"key":"B109","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1109\/taffc.2020.3031004","article-title":"Classification of interbeat interval time-series using attention entropy","volume":"14","author":"Yang","year":"2020","journal-title":"IEEE Trans. Affect. Comput."},{"key":"B110","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/j.bbe.2024.08.002","article-title":"Lead II electrocardiograph-derived entropy index for autonomic function assessment in type 2 diabetes mellitus","volume":"44","author":"Yang","year":"2024","journal-title":"Biocybern. Biomed. Eng."},{"key":"B111","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1016\/j.bbe.2025.08.006","article-title":"TRI-based heart rate variability parameterisation: advancing autonomic dysfunction assessment in diabetes and aging\u2014A cross-sectional observational study","volume":"45","author":"Yang","year":"2025","journal-title":"Biocybern. Biomed. Eng."},{"key":"B112","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102391","article-title":"Periodic distribution entropy: Unveiling the complexity of physiological time series through multidimensional dynamics","volume":"108","author":"Yu","year":"2024","journal-title":"Inf. Fusion"},{"key":"B113","doi-asserted-by":"publisher","DOI":"10.3390\/s22134890","article-title":"Diabetes detection and management through photoplethysmographic and electrocardiographic signals analysis: A systematic review","volume":"22","author":"Zanelli","year":"2022","journal-title":"Sensors"},{"key":"B114","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1089\/neu.2020.7249","article-title":"Association between physiological signal complexity and outcomes in moderate and severe traumatic brain injury: a CENTER-TBI exploratory analysis of multi-scale entropy","volume":"38","author":"Zeiler","year":"2021","journal-title":"J. Neurotrauma"},{"key":"B115","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.1016\/j.apmr.2024.06.014","article-title":"Enhancing spinal cord injury care: Using wearable technologies for physical activity, sleep, and cardiovascular health","volume":"105","author":"Zhao","year":"2024","journal-title":"Arch. Phys. Med. Rehabil."}],"container-title":["Frontiers in Physiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2026.1795118\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T22:10:32Z","timestamp":1775772632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2026.1795118\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,10]]},"references-count":115,"alternative-id":["10.3389\/fphys.2026.1795118"],"URL":"https:\/\/doi.org\/10.3389\/fphys.2026.1795118","relation":{},"ISSN":["1664-042X"],"issn-type":[{"value":"1664-042X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,10]]},"article-number":"1795118"}}