Predicting Survival in Heart Failure Case and Control Subjects by Use of Fully Automated Methods for Deriving Nonlinear and Conventional Indices of Heart Rate Dynamics
Abstract
Background Despite much recent interest in quantification of heart rate variability (HRV), the prognostic value of conventional measures of HRV and of newer indices based on nonlinear dynamics is not universally accepted. Methods and Results We have designed algorithms for analyzing ambulatory ECG recordings and measuring HRV without human intervention, using robust methods for obtaining time-domain measures (mean and SD of heart rate), frequency-domain measures (power in the bands of 0.001 to 0.01 Hz [VLF], 0.01 to 0.15 Hz [LF], and 0.15 to 0.5 Hz [HF] and total spectral power [TP] over all three of these bands), and measures based on nonlinear dynamics (approximate entropy [ApEn], a measure of complexity, and detrended fluctuation analysis [DFA], a measure of long-term correlations). The study population consisted of chronic congestive heart failure (CHF) case patients and sex- and age-matched control subjects in the Framingham Heart Study. After exclusion of technically inadequate studies and those with atrial fibrillation, we used these algorithms to study HRV in 2-hour ambulatory ECG recordings of 69 participants (mean age, 71.7±8.1 years). By use of separate Cox proportional-hazards models, the conventional measures SD ( P <.01), LF ( P <.01), VLF ( P <.05), and TP ( P <.01) and the nonlinear measure DFA ( P <.05) were predictors of survival over a mean follow-up period of 1.9 years; other measures, including ApEn ( P >.3), were not. In multivariable models, DFA was of borderline predictive significance ( P =.06) after adjustment for the diagnosis of CHF and SD. Conclusions These results demonstrate that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognostic value to complement traditional HRV measures.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A forced desynchrony study discovered that temporal correlations in heartbeat dynamics have a circadian rhythm (independent from scheduled behaviors, activity, and mean heart rate) with signs of breaking down towards patterns seen in congestive heart failure during the vulnerable morning period.
“A study using a forced desynchrony discovered that these temporal correlations have a circadian rhythm (independent from scheduled behaviors, activity, and mean HR) with signs of breaking down towards patterns seen in congestive heart failure during the vulnerable morning period (108).”
From Role of the circadian system in cardiovascular disease (2018) · cites this paper as [108]
Cites
Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use
Electrophysiology, T. F. O. T. E. S. O. C. T. N. A. (1996) · Circulation · doi:10.1161/01.cir.93.5.1043 · Cited by 17,159 · see citing works
Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a014868 · Cited by 15,289 · see citing works
Cox, D. R., & Oakes, D. (2018) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315137438 · Cited by 6,269 · see citing works
Kleiger, R. E., Miller, J. P., Bigger, J. T., & Moss, A. J. (1987) · The American Journal of Cardiology · doi:10.1016/0002-9149(87)90795-8 · Cited by 3,995 · see citing works
Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series
Peng, C., Havlin, S., Stanley, E., & Goldberger, A. L. (1995) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.166141 · Cited by 3,865 · see citing works
Self-citationThe natural history of congestive heart failure: the Framingham study
McKee, P. A., Castelli, W. P., McNamara, P. M., & Kannel, W. B. (1971) · New England Journal of Medicine · doi:10.1056/nejm197112232852601 · Cited by 3,448 · see citing works
Assessment of autonomic function in humans by heart rate spectral analysis
Pomeranz, B., Macaulay, R. J., Caudill, M. A., Kutz, I., Adam, D., Gordon, D., Kilborn, K. M., Barger, A. C., Shannon, D. C., Cohen, R. J., et al. (1985) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.1985.248.1.h151 · Cited by 2,876 · see citing works
Epidemiological approaches to heart disease: the Framingham study
Dawber, T. R., Meadors, G. F., & Moore, F. E. (1951) · American Journal of Public Health and the Nations Health · doi:10.2105/ajph.41.3.279 · Cited by 2,148 · see citing works
Survival after the onset of congestive heart failure in Framingham Heart Study subjects.
Ho, K. K. L., Anderson, K. M., Kannel, W. B., Grossman, W., & Levy, D. A. (1993) · Circulation · doi:10.1161/01.cir.88.1.107 · Cited by 1,865 · see citing works
Self-citationFrequency domain measures of heart period variability and mortality after myocardial infarction
Bigger, J. T., Fleiss, J. L., Steinman, R. C., Rolnitzky, L. M., Kleiger, R. E., & Rottman, J. N. (1992) · Circulation · doi:10.1161/01.cir.85.1.164 · Cited by 1,712 · see citing works
AN INVESTIGATION OF CORONARY HEART DISEASE IN FAMILIES
Kannel, W. B., Feinleib, M., McNAMARA, P. M., Garrison, R. J., & Castelli, W. P. (1979) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a112813 · Cited by 1,635 · see citing works
Physiological time-series analysis: what does regularity quantify?
Pincus, S. M., & Goldberger, A. L. (1994) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.1994.266.4.h1643 · Cited by 1,321 · see citing works
Self-citationReduced heart rate variability and mortality risk in an elderly cohort. The Framingham Heart Study
Tsuji, H., Venditti, F. J., Manders, E. S., Evans, J. C., Larson, M. G., Feldman, C. L., & Levy, D. (1994) · Circulation · doi:10.1161/01.cir.90.2.878 · Cited by 1,114 · see citing works
Self-citationLong-range anticorrelations and non-Gaussian behavior of the heartbeat
Peng, C., Mietus, J. E., Hausdorff, J. M., Havlin, S., Stanley, E., & Goldberger, A. L. (1993) · Physical Review Letters · doi:10.1103/physrevlett.70.1343 · Cited by 1,060 · see citing works
Self-citationNon-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside
Goldberger, A. L. (1996) · The Lancet · doi:10.1016/s0140-6736(96)90948-4 · Cited by 859 · see citing works
Self-citationSaul, J. P., Arai, Y., Berger, R. D., Lilly, L. S., Colucci, W. S., & Cohen, R. J. (1988) · The American Journal of Cardiology · doi:10.1016/0002-9149(88)91172-1 · Cited by 706 · see citing works
AN APPROACH TO LONGITUDINAL STUDIES IN A COMMUNITY: THE FRAMINGHAM STUDY
Dawber, T. R., Kannel, W. B., & Lyell, L. P. (1963) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1963.tb13299.x · Cited by 561 · see citing works
Lipsitz, L. A., Mietus, J. E., Moody, G. B., & Goldberger, A. L. (1990) · Circulation · doi:10.1161/01.cir.81.6.1803 · Cited by 519 · see citing works
Self-citationAging and the complexity of cardiovascular dynamics
Kaplan, D. T., Furman, M. I., Pincus, S. M., Ryan, S. M., Lipsitz, L. A., & Goldberger, A. L. (1991) · Biophysical Journal · doi:10.1016/s0006-3495(91)82309-8 · Cited by 457 · see citing works
Self-citationHayano, J., Sakakibara, Y., Yamada, M., Ohte, N., Fujinami, T., Yokoyama, K., Watanabe, Y., & Takata, K. (1990) · Circulation · doi:10.1161/01.cir.81.4.1217 · Cited by 381 · see citing works
Fractal mechanisms and heart rate dynamics
Peng, C.-K., Havlin, S., Hausdorff, J. M., Mietus, J. E., Stanley, H. E., & Goldberger, A. L. (1995) · Journal of Electrocardiology · doi:10.1016/s0022-0736(95)80017-4 · Cited by 358 · see citing works
(1991) · Institute for Nonlinear Science · doi:10.1007/978-1-4612-3118-9 · Cited by 163 · see citing works
Kaplan, D. T., & Talajic, M. R. (1991) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.165837 · Cited by 56 · see citing works
Effects of head-down bed rest on complex heart rate variability: response to LBNP testing
Goldberger, A. L., Mietus, J. E., Rigney, D. R., Wood, M. L., & FORTNEY, S. M. (1994) · Journal of Applied Physiology · doi:10.1152/jappl.1994.77.6.2863 · Cited by 49 · see citing works
Self-citationSubnormal heart period variability in heart failure: Effect of cardiac transplantation
Smith, M. L., Ellenbogen, K. A., Eckberg, D. L., Szentpetery, S., & Thames, M. D. (1989) · Journal of the American College of Cardiology · doi:10.1016/0735-1097(89)90059-4 · Cited by 35 · see citing works
Nonlinear Dynamics at the Bedside
Goldberger, A. L., & Rigney, D. R. (1991) · Institute for Nonlinear Science · doi:10.1007/978-1-4612-3118-9_22 · Cited by 29 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809 · see citing works
Self-citationPhysiological time-series analysis using approximate entropy and sample entropy
Richman, J. S., & Moorman, J. R. (2000) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2000.278.6.h2039 · Cited by 7,880 · see citing works
Heart rate variability: A review
Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., & Suri, J. S. (2006) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-006-0119-0 · Cited by 2,820 · see citing works
Multiscale entropy analysis of biological signals
Costa, M., Goldberger, A. L., & Peng, C.-K. (2005) · Physical Review E · doi:10.1103/physreve.71.021906 · Cited by 2,482 · see citing works
Self-citationFractal dynamics in physiology: Alterations with disease and aging
Goldberger, A. L., Amaral, L. A. N., Hausdorff, J. M., Ivanov, P. C., Peng, C., & Stanley, E. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.012579499 · Cited by 2,220 · see citing works
Self-citationDetecting long-range correlations with detrended fluctuation analysis
Kantelhardt, J. W., Koscielny–Bunde, E., Rêgo, H. H. A., Havlin, S., & Armin Bunde, A. (2001) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(01)00144-3 · Cited by 1,400 · see citing works
Effect of trends on detrended fluctuation analysis
Hu, K., Ivanov, P. C., Chen, Z., Carpena, P., & Stanley, E. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.64.011114 · Cited by 1,285 · see citing works
Effect of nonstationarities on detrended fluctuation analysis
Chen, Z., Ivanov, P. C., Hu, K., & Stanley, E. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.65.041107 · Cited by 957 · see citing works
Gait variability: methods, modeling and meaning
Hausdorff, J. M. (2005) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-2-19 · Cited by 942 · see citing works
NUNAN, D., SANDERCOCK, G. R. H., & BRODIE, D. A. (2010) · Pacing and Clinical Electrophysiology · doi:10.1111/j.1540-8159.2010.02841.x · Cited by 874 · see citing works
Crespo‐Leiro, M. G., Metra, M., Lund, L. H., Miličić, D., Costanzo, M. R., Filippatos, G. S., Gustafsson, F., Tsui, S. S. L., Barge‐Caballero, E., de Jonge, N., et al. (2018) · European Journal of Heart Failure · doi:10.1002/ejhf.1236 · Cited by 880 · see citing works
Hausdorff, J. M. (2007) · Human Movement Science · doi:10.1016/j.humov.2007.05.003 · Cited by 880 · see citing works
Heart Rate Variability ? A Historical Perspective
Billman, G. E. (2011) · Frontiers in Physiology · doi:10.3389/fphys.2011.00086 · Cited by 786 · see citing works
Sassi, R., Cerutti, S., Lombardi, F., Malik, M., Huikuri, H. V., Peng, C.-K., Schmidt, G., Yamamoto, Y., Reviewers:, D., Gorenek, B., et al. (2015) · Europace · doi:10.1093/europace/euv015 · Cited by 727 · see citing works
Self-citationHuikuri, H. V., Mäkikallio, T. H., Peng, C., Goldberger, A. L., Hintze, U., & Møller, M. (2000) · Circulation · doi:10.1161/01.cir.101.1.47 · Cited by 668 · see citing works
Self-citationDynamics of stability: the physiologic basis of functional health and frailty
Lipsitz, L. A. (2002) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/57.3.b115 · Cited by 646 · see citing works
Hausdorff, J. M. (2009) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3147408 · Cited by 633 · see citing works
Repeated hospitalizations predict mortality in the community population with heart failure
Setoguchi, S., Stevenson, L. W., & Schneeweiß, S. (2007) · American Heart Journal · doi:10.1016/j.ahj.2007.01.041 · Cited by 625 · see citing works
Methods derived from nonlinear dynamics for analysing heart rate variability
Voss, A., Schulz, S., Schroeder, R., Baumert, M., & Caminal, P. (2008) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2008.0232 · Cited by 595 · see citing works
Complex systems and the technology of variability analysis
SEELY, A. J. E., & Macklem, P. T. (2004) · Critical Care · doi:10.1186/cc2948 · Cited by 449 · see citing works
Role of the circadian system in cardiovascular disease
Thosar, S. S., Butler, M. P., & Shea, S. A. (2018) · Journal of Clinical Investigation · doi:10.1172/jci80590 · Cited by 443 · see citing works
Cardiac Interbeat Interval Dynamics From Childhood to Senescence
Pikkujämsä, S. M., Mäkikallio, T. H., Sourander, L., Räihä, I. J., Puukka, P. J., Skyttä, J., Peng, C., Goldberger, A. L., & Huikuri, H. V. (1999) · Circulation · doi:10.1161/01.cir.100.4.393 · Cited by 416 · see citing works
Self-citationNeural influences on cardiovascular variability: possibilities and pitfalls.
Malpas, S. C. (2002) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2002.282.1.h6 · Cited by 408 · see citing works
Asl, B. M., Setarehdan, S. K., & Mohebbi, M. (2008) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2008.04.007 · Cited by 384 · see citing works
Statistical physics and physiology: Monofractal and multifractal approaches
Stanley, E., Amaral, L. A. N., Goldberger, A. L., Havlin, S., Ivanov, P. C., & Peng, C. (1999) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(99)00230-7 · Cited by 368 · see citing works
Self-citationAging and nonlinear heart rate control in a healthy population
Beckers, F., Verheyden, B., & AUBERT, A. E. (2005) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00903.2005 · Cited by 338 · see citing works
Parati, G., Mancia, G., Rienzo, M. D., & Castiglioni, P. (2006) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00446.2006 · Cited by 325 · see citing works
Manor, B. D., Costa, M. D., Hu, K., Newton, E., Starobinets, O. V., Kang, H. G., Peng, C., Novak, V., & Lipsitz, L. A. (2010) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00390.2010 · Cited by 319 · see citing works
Self-citation70-year legacy of the Framingham Heart Study
Andersson, C., Johnson, A. D., Benjamin, E. J., Levy, D., & Vasan, R. S. (2019) · Nature Reviews Cardiology · doi:10.1038/s41569-019-0202-5 · Cited by 305 · see citing works
Self-citationFang, S.-C., Wu, Y.-L., & Tsai, P.-S. (2020) · Biological Research For Nursing · doi:10.1177/1099800419877442 · Cited by 302 · see citing works
Role of editing of R–R intervals in the analysis of heart rate variability
Peltola, M. A. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00148 · Cited by 302 · see citing works
Mäkikallio, T. H., Høiber, S., Køber, L., Torp-Pedersen, C., Peng, C., Goldberger, A. L., & Huikuri, H. V. (1999) · The American Journal of Cardiology · doi:10.1016/s0002-9149(98)01076-5 · Cited by 300 · see citing works
Self-citationQuantifying Fractal Dynamics of Human Respiration: Age and Gender Effects
Peng, C., Mietus, J. E., Liu, Y., Lee, C. K., Hausdorff, J. M., Stanley, E., Goldberger, A. L., & Lipsitz, L. A. (2002) · Annals of Biomedical Engineering · doi:10.1114/1.1481053 · Cited by 298 · see citing works
Self-citationGiamouzis, G., Kalogeropoulos, A. P., Georgiopoulou, V. V., Laskar, S. R., Smith, A. L., Dunbar, S. B., Triposkiadis, F. K., & Butler, J. (2010) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2010.08.010 · Cited by 282 · see citing works
Physiological Complexity, Aging, and the Path to Frailty
Lipsitz, L. A. (2004) · Science of Aging Knowledge Environment · doi:10.1126/sageke.2004.16.pe16 · Cited by 282 · see citing works
Measurement of heart rate variability: a clinical tool or a research toy?
Huikuri, H. V., Mäkikallio, T. H., Airaksinen, J. K. E., Mitrani, R. D., Castellanos, A., & Myerburg, R. J. (1999) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(99)00468-4 · Cited by 277 · see citing works
Comparison of Entropy and Complexity Measures for the Assessment of Depth of Sedation
Ferenets, R., Lipping, T., Anier, A., Jäntti, V., Melto, S., & Hovilehto, S. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.873543 · Cited by 262 · see citing works
Physiological Background of the Loss of Fractal Heart Rate Dynamics
Tulppo, M. P., Kiviniemi, A. M., Hautala, A. J., Kallio, M., Seppänen, T., Mäkikallio, T. H., & Huikuri, H. V. (2005) · Circulation · doi:10.1161/circulationaha.104.523712 · Cited by 254 · see citing works
Mäkikallio, T. H., Huikuri, H. V., Hintze, U., Videbæk, J., Mitrani, R. D., Castellanos, A., Myerburg, R. J., & Møller, M. (2001) · The American Journal of Cardiology · doi:10.1016/s0002-9149(00)01312-6 · Cited by 252 · see citing works
Vikman, S., Mäkikallio, T. H., Yli‐Mäyry, S., Pikkujämsä, S. M., Koivisto, A., Reinikainen, P., Airaksinen, J. K. E., & Huikuri, H. V. (1999) · Circulation · doi:10.1161/01.cir.100.20.2079 · Cited by 247 · see citing works
Prediction of sudden cardiac death by fractal analysis of heart rate variability in elderly subjects
Mäkikallio, T. H., Huikuri, H. V., Mäkikallio, A., Sourander, L., Mitrani, R. D., Castellanos, A., & Myerburg, R. J. (2001) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(01)01171-8 · Cited by 250 · see citing works
Effect of nonlinear filters on detrended fluctuation analysis
Chen, Z., Hu, K., Carpena, P., Bernaola‐Galván, P., Stanley, E., & Ivanov, P. C. (2005) · Physical Review E · doi:10.1103/physreve.71.011104 · Cited by 243 · see citing works
Fractal analysis of heart rate variability and mortality after an acute myocardial infarction
Tapanainen, J. M., Thomsen, P. E. B., Køber, L., Torp-Pedersen, C. T., Mäkikallio, T. H., Still, A.-M., Lindgren, K. S., & Huikuri, H. V. (2002) · The American Journal of Cardiology · doi:10.1016/s0002-9149(02)02488-8 · Cited by 239 · see citing works
Risk stratification in chronic heart failure
Cowburn, P. (1998) · European Heart Journal · doi:10.1053/euhj.1997.0820 · Cited by 217 · see citing works
Tulppo, M. P., Hughson, R. L., Mäkikallio, T. H., Airaksinen, J. K. E., Seppänen, T., & Huikuri, H. V. (2001) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2001.280.3.h1081 · Cited by 204 · see citing works
Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics
Huikuri, H. V., Perkiömäki, J. S., Maestri, R., & Pinna, G. D. (2009) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2008.0294 · Cited by 197 · see citing works
Ambulatory ECG and analysis of heart rate variability in Parkinson's disease
Haapaniemi, T. H. (2001) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.70.3.305 · Cited by 193 · see citing works
Physiologic complexity and aging: Implications for physical function and rehabilitation
Manor, B. D., & Lipsitz, L. A. (2012) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2012.08.020 · Cited by 194 · see citing works
Automatic Selection of the Threshold Value $r$ for Approximate Entropy
Lu, S. F., Chen, X., Kanters, J. K., Solomon, I. C., & Chon, K. H. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.919870 · Cited by 187 · see citing works
Ferrario, M., Signorini, M. G., Magenes, G., & Cerutti, S. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.859809 · Cited by 181 · see citing works
Traumatic Brain Injury Detection Using Electrophysiological Methods
Rapp, P. E., Keyser, D. O., Albano, A. M., Hernandez, R. S., Gibson, D. B., Zambon, R. A., Hairston, W. D., Hughes, J., Krystal, A. D., & Nichols, A. S. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00011 · Cited by 177 · see citing works
Approximate entropy for all signals
Chon, K. H., Scully, C. G., & Lu, S. F. (2009) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2009.934629 · Cited by 173 · see citing works
Magnitude and sign scaling in power-law correlated time series
Ashkenazy, Y., Havlin, S., Ivanov, P. C., Peng, C., Schulte-Frohlinde, V., & Stanley, E. (2003) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(03)00008-6 · Cited by 170 · see citing works
Self-citationMeasurement of heart rate variability by methods based on nonlinear dynamics
Huikuri, H. V., Mäkikallio, T. H., & Perkiömäki, J. S. (2003) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2003.09.021 · Cited by 169 · see citing works
Mäkikallio, T. H., Koistinen, J. M., Jordaens, L. J. L. M., Tulppo, M. P., Wood, N., Golosarsky, B., Peng, C., Goldberger, A. L., & Huikuri, H. V. (1999) · The American Journal of Cardiology · doi:10.1016/s0002-9149(98)01068-6 · Cited by 166 · see citing works
Self-citationHeart rate dynamics in refractory and well controlled temporal lobe epilepsy
Ansakorpi, H. (2002) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.72.1.26 · Cited by 162 · see citing works
Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review
Henriques, T. S., Ribeiro, M., Teixeira, A., Castro, L., Antunes, L. F., & Santos, C. (2020) · Entropy · doi:10.3390/e22030309 · Cited by 159 · see citing works
Arias-Londoño, J. D., Godino-Llorente, J. I., Sáenz-Lechón, N., Osma-Ruiz, V., & Castellanos-Dominguez, G. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2089052 · Cited by 155 · see citing works
Diminished chaos of heart rate time series in patients with major depression
Yeragani, V. K., Rao, K. A. R. K., Smitha, M., Pohl, R. B., Balon, R., & Srinivasan, K. (2002) · Biological Psychiatry · doi:10.1016/s0006-3223(01)01347-6 · Cited by 144 · see citing works
A New Chaotic Map With Dynamic Analysis and Encryption Application in Internet of Health Things
Tsafack, N., Sankar, S., Abd-El-Atty, B., Kengne, J., Jithin, K. C., Belazi, A., Mehmood, I., Bashir, A. K., Song, O.-Y., & El‐Latif, A. A. A. (2020) · IEEE Access · doi:10.1109/access.2020.3010794 · Cited by 148 · see citing works
Maestri, R., Pinna, G. D., Accardo, A., Allegrini, P., Balocchi, R., D’Addio, G., Ferrario, M., Menicucci, D., Porta, A., Sassi, R., et al. (2007) · Journal of Cardiovascular Electrophysiology · doi:10.1111/j.1540-8167.2007.00728.x · Cited by 146 · see citing works
Comprehensive analysis of cardiac health using heart rate signals
Acharya, U. R., Kannathal, N., & Krishnan, S. M. (2004) · Physiological Measurement · doi:10.1088/0967-3334/25/5/005 · Cited by 145 · see citing works
Mäkikallio, T. H., Ristimäe, T., Airaksinen, J. K. E., Peng, C., Goldberger, A. L., & Huikuri, H. V. (1998) · The American Journal of Cardiology · doi:10.1016/s0002-9149(97)00799-6 · Cited by 145 · see citing works
Self-citationDynamic Behavior of Heart Rate in Ischemic Stroke
Korpelainen, J. T., Sotaniemi, K. A., Mäkikallio, A., Huikuri, H. V., & Myllylä, V. V. (1999) · Stroke · doi:10.1161/01.str.30.5.1008 · Cited by 141 · see citing works
Endogenous circadian rhythm in an index of cardiac vulnerability independent of changes in behavior
Hu, K., Ivanov, P. C., Hilton, M. F., Chen, Z., Ayers, R. T., Stanley, H. E., & Shea, S. A. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408243101 · Cited by 141 · see citing works
Analysis of heart rate variability using fuzzy measure entropy
Liu, C., Li, K., Zhao, L., Liu, F., Zheng, D., Liu, C., & Liu, S. (2012) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2012.11.005 · Cited by 140 · see citing works
Scale-Independent Measures and Pathologic Cardiac Dynamics
Amaral, L. A. N., Goldberger, A. L., Ivanov, P. C., & Stanley, E. (1998) · Physical Review Letters · doi:10.1103/physrevlett.81.2388 · Cited by 137 · see citing works
Self-citationAcharya, U. R., Fujita, H., Sudarshan, V. K., Sree, V., Eugene, L. W. J., Ghista, D. N., & Tan, R. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.03.015 · Cited by 133 · see citing works
Sudden cardiac death: role of heart rate variability to identify patients at risk
Lombardi, F. (2001) · Cardiovascular Research · doi:10.1016/s0008-6363(01)00221-8 · Cited by 133 · see citing works
Effects of pharmacological adrenergic and vagal modulation on fractal heart rate dynamics
Tulppo, M. P., Mäkikallio, T. H., Seppänen, T., Shoemaker, J. K., Tutungi, E., Hughson, R. L., & Huikuri, H. V. (2001) · Clinical Physiology · doi:10.1046/j.1365-2281.2001.00344.x · Cited by 129 · see citing works
Sandercock, G., & Brodie, D. A. (2006) · Pacing and Clinical Electrophysiology · doi:10.1111/j.1540-8159.2006.00457.x · Cited by 130 · see citing works
Jović, A., & Bogunovic, N. (2010) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2010.09.005 · Cited by 126 · see citing works
Ivanov, P. C., Hu, K., Hilton, M. F., Shea, S. A., & Stanley, E. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709957104 · Cited by 125 · see citing works
Ma, Q. D. Y., Bartsch, R. P., Bernaola‐Galván, P., Yoneyama, M., & Ivanov, P. C. (2010) · Physical Review E · doi:10.1103/physreve.81.031101 · Cited by 124 · see citing works
NICOLINI, P., Ciulla, M. M., de Asmundis, C., Magrini, F., & Brugada, P. (2012) · Pacing and Clinical Electrophysiology · doi:10.1111/j.1540-8159.2012.03335.x · Cited by 122 · see citing works
Schmitt, D. T., & Ivanov, P. C. (2007) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00372.2007 · Cited by 122 · see citing works
State Anxiety and Nonlinear Dynamics of Heart Rate Variability in Students
Da, D., Владимировна, С. Е., & Dimitriev, A. D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0146131 · Cited by 121 · see citing works
EEG-based human emotion recognition using entropy as a feature extraction measure
Patel, P., Raghunandan, R., & Annavarapu, R. N. (2021) · Brain Informatics · doi:10.1186/s40708-021-00141-5 · Cited by 120 · see citing works
Is mental illness complex? From behavior to brain
Yang, A. C., & Tsai, S. (2012) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2012.09.015 · Cited by 118 · see citing works
Fractal and Complexity Measures of Heart Rate Variability
Perkiömäki, J. S., Mäkikallio, T. H., & Huikuri, H. V. (2005) · Clinical and Experimental Hypertension · doi:10.1081/ceh-48742 · Cited by 117 · see citing works
Noise and Complexity in Human Postural Control: Interpreting the Different Estimations of Entropy
Rhea, C. K., Silver, T. A., Hong, S. L., Ryu, J. H., Studenka, B. E., Hughes, C. M. L., & Haddad, J. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017696 · Cited by 118 · see citing works
Ho, Y., Lin, C., Lin, Y., & Lo, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018699 · Cited by 112 · see citing works
A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals
Ponsiglione, A. M., Cosentino, C., Cesarelli, G., Amato, F., & Romano, M. (2021) · Sensors · doi:10.3390/s21186136 · Cited by 112 · see citing works
NONLINEAR METHODS OF CARDIOVASCULAR PHYSICS AND THEIR CLINICAL APPLICABILITY
Wessel, N., Malberg, H., Bauernschmitt, R., & Kurths, J. (2007) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127407019093 · Cited by 111 · see citing works
Hussain, L. (2018) · Cognitive Neurodynamics · doi:10.1007/s11571-018-9477-1 · Cited by 105 · see citing works
Suppressed circadian heart rate dynamics in temporal lobe epilepsy
Ronkainen, E. (2005) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2004.053777 · Cited by 105 · see citing works
Physiological basis of fractal complexity properties of heart rate variability in man
Francis, D. P., Willson, K., Γεωργιάδου, Π., Wensel, R., Davies, C., Coats, A. J. S., & Piepoli, M. F. (2002) · The Journal of Physiology · doi:10.1113/jphysiol.2001.013389 · Cited by 102 · see citing works
Liu, C., Liu, C., Shao, P., Li, L., Sun, X., Wang, X., & Liu, F. (2010) · Physiological Measurement · doi:10.1088/0967-3334/32/2/002 · Cited by 103 · see citing works
Martinikorena, I., Martínez-Ramírez, A., Gómez, M., Lecumberri, P., Casas-Herrero, A., Cadore, E. L., Millor, N., Zambom-Ferraresi, F., Idoate, F., & Izquierdo, M. (2016) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2015.09.015 · Cited by 97 · see citing works
NONLINEAR ANALYSIS OF PHYSIOLOGICAL SIGNALS: A REVIEW
Faust, O., & Bairy, G. M. (2012) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519412400155 · Cited by 98 · see citing works
Schmitt, D. T., Stein, P. K., & Ivanov, P. C. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2014819 · Cited by 97 · see citing works
Lin, A., Liu, K. K. L., Bartsch, R. P., & Ivanov, P. C. (2016) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2015.0182 · Cited by 96 · see citing works
Yeragani, V. K., Radhakrishna, R. K. A., Tancer, M. E., & Uhde, T. W. (2002) · Neuropsychobiology · doi:10.1159/000066388 · Cited by 97 · see citing works
Interpretation of heart rate variability via detrended fluctuation analysis and αβ filter
Echeverría, J. C., Woolfson, M. S., Crowe, J. A., Hayes-Gill, B. R., Croaker, G. D. H., & Vyas, H. (2003) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.1562051 · Cited by 94 · see citing works
Clinical Applicability of Heart Rate Variability Analysis by Methods Based on Nonlinear Dynamics
Mäkikallio, T. H., Tapanainen, J. M., Tulppo, M. P., & Huikuri, H. V. (2002) · Cardiac Electrophysiology Review · doi:10.1023/a:1016381025759 · Cited by 93 · see citing works
Heart-Rate Complexity for Prediction of Prehospital Lifesaving Interventions in Trauma Patients
Cancio, L. C., Batchinsky, A. I., Salinas, J., Kuusela, T., Convertino, V. A., Wade, C. E., & Holcomb, J. B. (2008) · The Journal of Trauma: Injury, Infection, and Critical Care · doi:10.1097/ta.0b013e3181848241 · Cited by 91 · see citing works
Linear and non-linear 24 h heart rate variability in chronic heart failure
Guzzetti, S., Mezzetti, S., Magatelli, R., Porta, A., Angelis, G. D., Rovelli, G., & Malliani, A. (2000) · Autonomic Neuroscience · doi:10.1016/s1566-0702(00)00239-3 · Cited by 87 · see citing works
Wayne, P. M., Manor, B. D., Novak, V., Costa, M. D., Hausdorff, J. M., Goldberger, A. L., Ahn, A. C., Yeh, G. Y., Peng, C., Lough, M., et al. (2012) · Contemporary Clinical Trials · doi:10.1016/j.cct.2012.09.006 · Cited by 87 · see citing works
Self-citationDeterminants and interindividual variation of R-R interval dynamics in healthy middle-aged subjects
Pikkujämsä, S. M., Mäkikallio, T. H., Airaksinen, J. K. E., & Huikuri, H. V. (2001) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2001.280.3.h1400 · Cited by 85 · see citing works
Narin, A., İşler, Y., & Özer, M. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.11.016 · Cited by 84 · see citing works
- Authors
- Ho, K. K. L., Moody, G. B., Peng, C.-K., Mietus, J. E., Larson, M. G., Levy, D., & Goldberger, A. L.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Venue
- Circulation
- Published
- 1997-08-05
- Volume
- 96
- Issue
- 3
- Pages
- 842-848
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W2006524496
- Cited by
- 489
- References
- 26
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 9 · 2022: 6 · 2021: 12 · 2020: 24 · 2019: 19 · 2018: 17 · 2017: 17 · 2016: 18 · 2015: 18 · 2014: 13 · 2013: 21 · 2012: 26
- FWCI
- 9.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r29mh054081-03, n01hc038038-022)
- National Heart, Lung, and Blood Institute (N01-HC-38038)
- National Institute of Mental Health (MH-54081)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- heart rate variability, congestive heart failure, survival prediction, prognostic factors, nonlinear HRV indices, detrended fluctuation analysis, approximate entropy, autonomic nervous system, Holter monitoring, frequency-domain HRV measures
- MeSH terms
- Aged, Aged, 80 and over, Algorithms, Automation, Feasibility Studies, Female, Forecasting, Heart Failure, Heart Rate, Humans, Male, Middle Aged, Models, Cardiovascular, Reference Values, Electrocardiography, Ambulatory, Survival Analysis, Nonlinear Dynamics, Entropy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
