{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T21:08:39Z","timestamp":1780088919732,"version":"3.54.0"},"reference-count":83,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.bspc.2026.110575","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T03:10:43Z","timestamp":1778814643000},"page":"110575","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["Evolution of Heart Rate Variability (HRV) Analysis Methodologies (2000\u20132025): From Task Force Standards to Wearables, AI, and Clinical Translation"],"prefix":"10.1016","volume":"123","author":[{"given":"Ziqiang","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanli","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110575_b0005","doi-asserted-by":"crossref","first-page":"258","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":"10.1016\/j.bspc.2026.110575_b0010","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1093\/europace\/euv015","article-title":"Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society","volume":"17","author":"Sassi","year":"2015","journal-title":"Europace"},{"key":"10.1016\/j.bspc.2026.110575_b0015","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1111\/j.1469-8986.1997.tb02140.x","article-title":"Heart rate variability: origins, methods, and interpretive caveats","volume":"34","author":"Berntson","year":"1997","journal-title":"Psychophysiology"},{"key":"10.1016\/j.bspc.2026.110575_b0020","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1111\/j.1469-8986.1993.tb01731.x","article-title":"Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications","volume":"30","author":"Berntson","year":"1993","journal-title":"Psychophysiology"},{"key":"10.1016\/j.bspc.2026.110575_b0025","doi-asserted-by":"crossref","first-page":"86","DOI":"10.3389\/fphys.2011.00086","article-title":"Heart rate variability - a historical perspective","volume":"2","author":"Billman","year":"2011","journal-title":"Front Physiol"},{"key":"10.1016\/j.bspc.2026.110575_b0030","doi-asserted-by":"crossref","first-page":"66","DOI":"10.12710\/cardiometry.2017.10.6676","article-title":"Heart rate variability analysis: physiological foundations and main methods","volume":"10","author":"Baevsky","year":"2017","journal-title":"Cardiometry"},{"key":"10.1016\/j.bspc.2026.110575_b0035","first-page":"1043","article-title":"standards of measurement, physiological interpretation and clinical use","volume":"93","author":"Heart rate variability","year":"1996","journal-title":"Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Circulation"},{"key":"10.1016\/j.bspc.2026.110575_b0040","doi-asserted-by":"crossref","first-page":"265","DOI":"10.3389\/fpubh.2017.00265","article-title":"Hidden Signals-The History and Methods of Heart Rate Variability","volume":"5","author":"Ernst","year":"2017","journal-title":"Front Public Health"},{"key":"10.1016\/j.bspc.2026.110575_b0045","first-page":"814","article-title":"ELECTRONIC EVALUATION OF THE FETAL HEART RATE. VIII. PATTERNS PRECEDING FETAL DEATH, FURTHER OBSERVATIONS","volume":"87","author":"Hon","year":"1963","journal-title":"Am J Obstet Gynecol"},{"key":"10.1016\/j.bspc.2026.110575_b0050","doi-asserted-by":"crossref","first-page":"52","DOI":"10.5694\/j.1326-5377.1978.tb131339.x","article-title":"Sinus arrhythmia in acute myocardial infarction","volume":"2","author":"Wolf","year":"1978","journal-title":"Med J Aust"},{"key":"10.1016\/j.bspc.2026.110575_b0055","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/0002-9149(87)90795-8","article-title":"Decreased heart rate variability and its association with increased mortality after acute myocardial infarction","volume":"59","author":"Kleiger","year":"1987","journal-title":"Am J Cardiol"},{"key":"10.1016\/j.bspc.2026.110575_b0060","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1161\/01.CIR.90.2.878","article-title":"Reduced heart rate variability and mortality risk in an elderly cohort","volume":"90","author":"Tsuji","year":"1994","journal-title":"The Framingham Heart Study, Circulation"},{"key":"10.1016\/j.bspc.2026.110575_b0065","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1136\/bmj.1.6106.145","article-title":"Immediate heart-rate response to standing: simple test for autonomic neuropathy in diabetes","volume":"1","author":"Ewing","year":"1978","journal-title":"Br Med J"},{"key":"10.1016\/j.bspc.2026.110575_b0070","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1136\/bmj.285.6346.916","article-title":"Diagnosis and management of diabetic autonomic neuropathy","volume":"285","author":"Ewing","year":"1982","journal-title":"Br Med J (Clin Res Ed)"},{"key":"10.1016\/j.bspc.2026.110575_b0075","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1126\/science.6166045","article-title":"Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control","volume":"213","author":"Akselrod","year":"1981","journal-title":"Science"},{"key":"10.1016\/j.bspc.2026.110575_b0080","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0182611","article-title":"The very low-frequency band of heart rate variability represents the slow recovery component after a mental stress task","volume":"12","author":"Usui","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2026.110575_b0085","doi-asserted-by":"crossref","first-page":"1878","DOI":"10.1016\/S0735-1097(99)00468-4","article-title":"Measurement of heart rate variability: a clinical tool or a research toy?","volume":"34","author":"Huikuri","year":"1999","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.bspc.2026.110575_b0090","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real-time QRS detection algorithm","volume":"32","author":"Pan","year":"1985","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.bspc.2026.110575_b0095","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1006\/cbmr.1998.1502","article-title":"Design of a PC-based system for time-domain and spectral analysis of heart rate variability","volume":"32","author":"Adelmann","year":"1999","journal-title":"Comput Biomed Res"},{"key":"10.1016\/j.bspc.2026.110575_b0100","doi-asserted-by":"crossref","first-page":"13","DOI":"10.5298\/1081-5937-41.1.05","article-title":"Heart Rate Variability Anatomy and Physiology","volume":"41","author":"Shaffer","year":"2013","journal-title":"Biofeedback"},{"key":"10.1016\/j.bspc.2026.110575_b0105","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1042\/cs0910201","article-title":"Poincare plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans","volume":"91","author":"Kamen","year":"1996","journal-title":"Clin Sci (Lond)"},{"key":"10.1016\/j.bspc.2026.110575_b0110","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.cmpb.2013.07.024","article-title":"Kubios HRV\u2013heart rate variability analysis software","volume":"113","author":"Tarvainen","year":"2014","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.bspc.2026.110575_b0115","doi-asserted-by":"crossref","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":"10.1016\/j.bspc.2026.110575_b0120","doi-asserted-by":"crossref","first-page":"634","DOI":"10.3109\/03091900903150998","article-title":"A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects","volume":"33","author":"Lu","year":"2009","journal-title":"J Med Eng Technol"},{"key":"10.1016\/j.bspc.2026.110575_b0125","doi-asserted-by":"crossref","first-page":"779","DOI":"10.3389\/fphys.2020.00779","article-title":"Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses","volume":"11","author":"Mejia-Mejia","year":"2020","journal-title":"Front Physiol"},{"key":"10.1016\/j.bspc.2026.110575_b0130","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s40101-020-00233-x","article-title":"Pulse rate variability: a new biomarker, not a surrogate for heart rate variability","volume":"39","author":"Yuda","year":"2020","journal-title":"J Physiol Anthropol"},{"key":"10.1016\/j.bspc.2026.110575_b0135","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1007\/s40279-016-0484-2","article-title":"Monitoring Athletic Training Status Through Autonomic Heart Rate Regulation: A Systematic Review and Meta-Analysis","volume":"46","author":"Bellenger","year":"2016","journal-title":"Sports Med"},{"key":"10.1016\/j.bspc.2026.110575_b0140","article-title":"Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability","volume":"15","author":"Byun","year":"2024","journal-title":"Front Psychiatry"},{"key":"10.1016\/j.bspc.2026.110575_b0145","first-page":"743","article-title":"A Critical Review of Consumer Wearables","volume":"9","author":"Peake","year":"2018","journal-title":"Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations, Front Physiol"},{"key":"10.1016\/j.bspc.2026.110575_b0150","doi-asserted-by":"crossref","DOI":"10.3389\/fdgth.2021.639444","article-title":"Trends in Heart-Rate Variability Signal Analysis","volume":"3","author":"Ishaque","year":"2021","journal-title":"Front Digit Health"},{"key":"10.1016\/j.bspc.2026.110575_b0155","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.1016\/j.jacc.2024.03.401","article-title":"Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week","volume":"83","author":"Jain","year":"2024","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.bspc.2026.110575_b0160","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.hrthm.2023.10.019","article-title":"Up digital and personal: How heart digital twins can transform heart patient care","volume":"21","author":"Trayanova","year":"2024","journal-title":"Heart Rhythm"},{"key":"10.1016\/j.bspc.2026.110575_b0165","first-page":"213","article-title":"Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research - Recommendations for Experiment Planning","volume":"8","author":"Laborde","year":"2017","journal-title":"Data Analysis, and Data Reporting, Front Psychol"},{"key":"10.1016\/j.bspc.2026.110575_b0170","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"10.1016\/j.bspc.2026.110575_b0175","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.3390\/s23031625","article-title":"Algorithm for Mobile Platform-Based Real-Time QRS Detection","volume":"23","author":"Neri","year":"2023","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.bspc.2026.110575_b0180","first-page":"3501","article-title":"Software tools for heart rate variability analysis, International Journal of Recent","volume":"6","author":"Singh","year":"2015","journal-title":"Scientific Research"},{"key":"10.1016\/j.bspc.2026.110575_b0185","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.cmpb.2004.03.004","article-title":"Software for advanced HRV analysis","volume":"76","author":"Niskanen","year":"2004","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.bspc.2026.110575_b0190","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0118308","article-title":"Short-term heart rate variability\u2013influence of gender and age in healthy subjects","volume":"10","author":"Voss","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2026.110575_b0195","doi-asserted-by":"crossref","DOI":"10.1155\/2012\/219080","article-title":"Linear and nonlinear heart rate variability indexes in clinical practice","author":"Francesco","year":"2012","journal-title":"Comput Math Methods Med"},{"key":"10.1016\/j.bspc.2026.110575_b0200","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/1475-925X-10-90","article-title":"Review and classification of variability analysis techniques with clinical applications","volume":"10","author":"Bravi","year":"2011","journal-title":"Biomed Eng Online"},{"key":"10.1016\/j.bspc.2026.110575_b0205","first-page":"H1643","article-title":"Physiological time-series analysis: what does regularity quantify?","volume":"266","author":"Pincus","year":"1994","journal-title":"Am J Physiol"},{"key":"10.1016\/j.bspc.2026.110575_b0210","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1063\/1.166141","article-title":"Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series","volume":"5","author":"Peng","year":"1995","journal-title":"Chaos"},{"key":"10.1016\/j.bspc.2026.110575_b0215","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1519\/JSC.0000000000001800","article-title":"Heart Rate Variability During Exercise: A Comparison of Artefact Correction Methods","volume":"32","author":"Giles","year":"2018","journal-title":"J Strength Cond Res"},{"key":"10.1016\/j.bspc.2026.110575_b0220","doi-asserted-by":"crossref","first-page":"7146","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":"10.1016\/j.bspc.2026.110575_b0225","first-page":"561","article-title":"Multi-Site Pulse Transit Times, Beat-to-Beat Blood Pressure, and Isovolumic Contraction Time at Rest and Under Stressors, IEEE J Biomed Health","volume":"26","author":"Di Rienzo","year":"2022","journal-title":"Inform"},{"key":"10.1016\/j.bspc.2026.110575_b0230","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/ab840a","article-title":"Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG","volume":"41","author":"Kinnunen","year":"2020","journal-title":"Physiol Meas"},{"key":"10.1016\/j.bspc.2026.110575_b0235","doi-asserted-by":"crossref","first-page":"960","DOI":"10.30773\/pi.2020.0168","article-title":"Heart Rate Variability Analysis: How Much Artifact Can We Remove?","volume":"17","author":"Sheridan","year":"2020","journal-title":"Psychiatry Investig"},{"key":"10.1016\/j.bspc.2026.110575_b0240","doi-asserted-by":"crossref","first-page":"241","DOI":"10.3390\/s24216826","article-title":"Assessment of Physiological Signals from Photoplethysmography Sensors Compared to an Electrocardiogram Sensor: A Validation Study in Daily Life","volume":"24","author":"Rehman","year":"2024","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.bspc.2026.110575_b0245","first-page":"49","volume":"2","author":"Jensen","year":"2021","journal-title":"Health"},{"key":"10.1016\/j.bspc.2026.110575_b0250","article-title":"Accuracy of 11 Wearable","volume":"11","author":"Lee","year":"2023","journal-title":"Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study, JMIR Mhealth Uhealth"},{"key":"10.1016\/j.bspc.2026.110575_b0255","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.3390\/s22166317","article-title":"A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults","volume":"22","author":"Miller","year":"2022","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.bspc.2026.110575_b0260","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1056\/NEJMra2307160","article-title":"Key Issues as Wearable Digital Health Technologies Enter Clinical Care","volume":"390","author":"Ginsburg","year":"2024","journal-title":"N Engl J Med"},{"key":"10.1016\/j.bspc.2026.110575_b0265","doi-asserted-by":"crossref","unstructured":"C. Leclercq, H. Witt, G. Hindricks, R.P. Katra, D. Albert, A. Belliger, M.R. Cowie, T. Deneke, P. Friedman, M. Haschemi, T. Lobban, I. Lordereau, M.V. McConnell, L. Rapallini, E. Samset, M.P. Turakhia, J.P. Singh, E. Svennberg, M. Wadhwa, F. Weidinger, Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: Proceedings of the European Society of Cardiology Cardiovascular Round Table, Europace, 24 (2022) 1372-1383.","DOI":"10.1093\/europace\/euac052"},{"key":"10.1016\/j.bspc.2026.110575_b0270","doi-asserted-by":"crossref","DOI":"10.2196\/18907","article-title":"Wearable Health Devices in Health Care: Narrative Systematic Review","volume":"8","author":"Lu","year":"2020","journal-title":"JMIR Mhealth Uhealth"},{"key":"10.1016\/j.bspc.2026.110575_b0275","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/s24134316","article-title":"Driver Fatigue Detection Using Heart Rate Variability Features from 2-Minute Electrocardiogram Signals While Accounting for Sex Differences","volume":"24","author":"Zeng","year":"2024","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.bspc.2026.110575_b0280","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1093\/europace\/euac135","article-title":"Machine learning in sudden cardiac death risk prediction: a systematic review","volume":"24","author":"Barker","year":"2022","journal-title":"Europace"},{"key":"10.1016\/j.bspc.2026.110575_b0285","article-title":"Development of Enhanced Machine Learning Models for Predicting Type 2 Diabetes Mellitus Using Heart Rate Variability: A Retrospective Study","volume":"17","author":"Fengade","year":"2025","journal-title":"Cureus"},{"key":"10.1016\/j.bspc.2026.110575_b0290","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ"},{"key":"10.1016\/j.bspc.2026.110575_b0295","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"},{"key":"10.1016\/j.bspc.2026.110575_b0300","article-title":"Improving clinical decision support through interpretable machine learning and error handling in electronic health records","author":"Arora","year":"2025","journal-title":"J Am Med Inform Assoc"},{"key":"10.1016\/j.bspc.2026.110575_b0305","first-page":"e376","article-title":"The European artificial intelligence strategy: implications and challenges for digital health, Lancet Digit","volume":"2","author":"Cohen","year":"2020","journal-title":"Health"},{"key":"10.1016\/j.bspc.2026.110575_b0310","first-page":"e745","article-title":"The false hope of current approaches to explainable artificial intelligence in health care, Lancet Digit","volume":"3","author":"Ghassemi","year":"2021","journal-title":"Health"},{"key":"10.1016\/j.bspc.2026.110575_b0315","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/0002-9149(87)90601-1","article-title":"Heart rate variability as an index of sympathovagal interaction after acute myocardial infarction","volume":"60","author":"Lombardi","year":"1987","journal-title":"Am J Cardiol"},{"key":"10.1016\/j.bspc.2026.110575_b0320","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1161\/01.CIR.0000047275.25795.17","article-title":"Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients","volume":"107","author":"La Rovere","year":"2003","journal-title":"Circulation"},{"key":"10.1016\/j.bspc.2026.110575_b0325","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1161\/01.CIR.98.15.1510","article-title":"Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial","volume":"98","author":"Nolan","year":"1998","journal-title":"Circulation"},{"key":"10.1016\/j.bspc.2026.110575_b0330","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.jad.2024.03.071","article-title":"The effect of paroxetine on heart rate variability in patients with major depressive disorder: A systematic review and meta-analysis","volume":"355","author":"de Oliveira","year":"2024","journal-title":"J Affect Disord"},{"key":"10.1016\/j.bspc.2026.110575_b0335","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1111\/pcn.13356","article-title":"Heart rate variability in patients with anxiety disorders: A systematic review and meta-analysis","volume":"76","author":"Cheng","year":"2022","journal-title":"Psychiatry Clin Neurosci"},{"key":"10.1016\/j.bspc.2026.110575_b0340","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s12160-009-9101-z","article-title":"Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health","volume":"37","author":"Thayer","year":"2009","journal-title":"Ann Behav Med"},{"key":"10.1016\/j.bspc.2026.110575_b0345","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1038\/s41398-025-03339-x","article-title":"Heart rate variability in mental disorders: an umbrella review of meta-analyses","volume":"15","author":"Wang","year":"2025","journal-title":"Transl Psychiatry"},{"key":"10.1016\/j.bspc.2026.110575_b0350","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s00508-021-01991-z","article-title":"Virtual reality biofeedback interventions for treating anxiety : A systematic review, meta-analysis and future perspective","volume":"134","author":"Kothgassner","year":"2022","journal-title":"Wien Klin Wochenschr"},{"key":"10.1016\/j.bspc.2026.110575_b0355","doi-asserted-by":"crossref","first-page":"6650","DOI":"10.1038\/s41598-021-86149-7","article-title":"A meta-analysis on heart rate variability biofeedback and depressive symptoms","volume":"11","author":"Pizzoli","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.bspc.2026.110575_b0360","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s41105-024-00563-8","article-title":"A review of automatic sleep stage classification using machine learning algorithms based on heart rate variability","volume":"23","author":"Yu","year":"2025","journal-title":"Sleep Biol Rhythms"},{"key":"10.1016\/j.bspc.2026.110575_b0365","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1109\/TBME.2003.817636","article-title":"Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea","volume":"50","author":"Penzel","year":"2003","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.bspc.2026.110575_b0370","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.sleep.2024.07.034","article-title":"Heart rate variability during sleep onset in patients with insomnia with or without comorbid sleep apnea","volume":"122","author":"Ma","year":"2024","journal-title":"Sleep Med"},{"key":"10.1016\/j.bspc.2026.110575_b0375","doi-asserted-by":"crossref","first-page":"113","DOI":"10.5960\/dzsm.2024.595","article-title":"Heart Rate Variability \u2013 Methods and Analysis in Sports Medicine and Exercise Science","volume":"75","author":"Gronwald","year":"2024","journal-title":"Deutsche Zeitschrift f\u00fcr Sportmedizin"},{"key":"10.1016\/j.bspc.2026.110575_b0380","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s40279-013-0071-8","article-title":"Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring","volume":"43","author":"Plews","year":"2013","journal-title":"Sports Med"},{"key":"10.1016\/j.bspc.2026.110575_b0385","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s00421-007-0552-2","article-title":"Endurance training guided individually by daily heart rate variability measurements","volume":"101","author":"Kiviniemi","year":"2007","journal-title":"Eur J Appl Physiol"},{"key":"10.1016\/j.bspc.2026.110575_b0390","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1038\/s41746-021-00533-1","article-title":"Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms","volume":"4","author":"Gadaleta","year":"2021","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.bspc.2026.110575_b0395","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1093\/ehjdh\/ztae055","article-title":"Long-term adherence to a wearable for continuous behavioural activity measuring in the SafeHeart implantable cardioverter defibrillator population","volume":"5","author":"Frodi","year":"2024","journal-title":"Eur Heart J Digit Health"},{"key":"10.1016\/j.bspc.2026.110575_b0400","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.cvdhj.2021.02.001","article-title":"\u201cWearables only work on patients that wear them\u201d: Barriers and facilitators to the adoption of wearable cardiac monitoring technologies","volume":"2","author":"Ferguson","year":"2021","journal-title":"Cardiovasc Digit Health J"},{"key":"10.1016\/j.bspc.2026.110575_b0405","doi-asserted-by":"crossref","DOI":"10.2196\/69544","article-title":"Advancing Health Care With Digital Twins: Meta-Review of Applications and Implementation Challenges","volume":"27","author":"Ringeval","year":"2025","journal-title":"J Med Internet Res"},{"key":"10.1016\/j.bspc.2026.110575_b0410","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1038\/s41746-024-01073-0","article-title":"Digital twins for health: a scoping review","volume":"7","author":"Katsoulakis","year":"2024","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.bspc.2026.110575_b0415","doi-asserted-by":"crossref","first-page":"4808","DOI":"10.1093\/eurheartj\/ehae619","article-title":"Cardiovascular care with digital twin technology in the era of generative artificial intelligence","volume":"45","author":"Thangaraj","year":"2024","journal-title":"Eur Heart J"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426011298?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426011298?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:14:31Z","timestamp":1780085671000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426011298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":83,"alternative-id":["S1746809426011298"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110575","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evolution of Heart Rate Variability (HRV) Analysis Methodologies (2000\u20132025): From Task Force Standards to Wearables, AI, and Clinical Translation","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110575","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110575"}}