The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Fractal mechanisms and heart rate dynamics
Open accessAbstract
Under healthy conditions, the normal cardiac (sinus) interbeat interval fluctuates in a complex manner. Quantitative analysis using techniques adapted from statistical physics reveals the presence of long-range power-law correlations extending over thousands of heartbeats. This scale-invariant (fractal) behavior suggests that the regulatory system generating these fluctuations is operating far from equilibrium. In contrast, it is found that for subjects at high risk of sudden death (e.g., congestive heart failure patients), these long-range correlations break down. Application of fractal scaling analysis and related techniques provides new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as motivating development of novel physiologic models of systems that appear to be heterodynamic rather than homeostatic.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
In the citing paper
“This fractal pattern breaks down with disease and its change is predictive of mortality”
From The impact of the circadian timing system on cardiovascular and metabolic function (2012) · cites this paper as Peng et al., 1995
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Fractal Geometry of Nature
Mandelbrot, B. B., & WHEELER, J. A. (1983) · American Journal of Physics · doi:10.1119/1.13295 · Cited by 21,964
Self-organized criticality: An explanation of the 1/ f noise
Bak, P., Tang, C., & Wiesenfeld, K. A. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.381 · Cited by 7,644
The Fractal Geometry of Nature
Mandelbrot, B. B., & Longley, P. (1982) · CERN Document Server (European Organization for Nuclear Research) · Cited by 7,293
Introduction to Phase Transitions and Critical Phenomena
Stanley, H. E., & Wong, V. K. (1972) · American Journal of Physics · doi:10.1119/1.1986710 · Cited by 4,108
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
Introduction to Phase Transitions and Critical Phenomena
Stanley, E., & Ahlers, G. (1973) · Physics Today · doi:10.1063/1.3127900 · Cited by 3,690
Statistics for Long-Memory Processes.
Ray, W. D., & Beran, J. (1996) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2983481 · Cited by 2,716
Organization for physiological homeostasis
Cannon, W. B. (1929) · Physiological Reviews · doi:10.1152/physrev.1929.9.3.399 · Cited by 1,844
Statistics for Long-Memory Processes.
Percival, D. B., & Beran, J. (1996) · Journal of the American Statistical Association · doi:10.2307/2291761 · Cited by 1,504
Long-range correlations in nucleotide sequences
Peng, C., Buldyrev, S. V., Goldberger, A. L., Havlin, S., Sciortino, F., Simons, M., & Stanley, E. (1992) · Nature · doi:10.1038/356168a0 · Cited by 1,413
Research works printed but not linked: 2
- Peng, 1994, On the mosaic organization of DNA sequences, Phys Rev Lett, 49, 1685
- Press, 1978, Flicker noise in astronomy and elsewhere, Comments Astrophys, 7, 103
Cited by
Sorted by citations
Items 201–300 of 358
Koichubekov, B., Петрович, Р. В., Sorokina, M., Korshukov, I., Turgunova, L., Laryushina, Y. M., Bakirova, R., Мендигереевна, М. Г., Bekov, E. K., & Kultenova, M. (2017) · Entropy · doi:10.3390/e19100523 · Cited by 12
Metabolic imaging in multiple time scales
Ramanujan, V. K. (2013) · Methods · doi:10.1016/j.ymeth.2013.08.027 · Cited by 11
Liu, H., Zhan, P., Shi, J., Hu, M., Wang, G., & Wang, W. (2020) · BMC Nephrology · doi:10.1186/s12882-020-02196-8 · Cited by 11
Insight into the hierarchical control governing leg stiffness during the stance phase of running
Garofolini, A., Mickle, K. J., McLaughlin, P. A., & Taylor, S. B. (2022) · Scientific Reports · doi:10.1038/s41598-022-16263-7 · Cited by 15
Šapina, M., Garcin, M., Kramarić, K., Milas, K., Brdarić, D., & Pirić, M. (2020) · Fluctuation and Noise Letters · doi:10.1142/s0219477520500261 · Cited by 11
Castiglioni, P., Merati, G., Parati, G., & Faini, A. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab2b4a · Cited by 11
Du, E. W., Tan, H. S., Tan, C. W., Sultana, R., & Sng, B. L. (2021) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000001551 · Cited by 10
A simple approach to determine loss of physiological complexity in heart rate series
Özel, H. F., & Kazdağlı, H. (2023) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/acd254 · Cited by 10
Benefits of Zero-Phase or Linear Phase Filters to Design Multiscale Entropy: Theory and Application
Grivel, É., Berthelot, B., Colin, G., Legrand, P., & Ibanez, V. (2024) · Entropy · doi:10.3390/e26040332 · Cited by 10
Sleep Network Deterioration as a Function of Dim-Light-At-Night Exposure Duration in a Mouse Model
Panagiotou, M., Rohling, J. H. T., & Deboer, T. (2020) · Clocks & Sleep · doi:10.3390/clockssleep2030023 · Cited by 9
Self-Similarity and Detrended Fluctuation Analysis of Cardiovascular Signals
Castiglioni, P., Rienzo, M. D., & Faini, A. (2017) · Springer eBooks · doi:10.1007/978-3-319-58709-7_7 · Cited by 10
Bian, C., & Ning, X. (2004) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2004.01.040 · Cited by 10
Is virtual reality a useful tool in the teaching of physiology?
Richardson, D. R. (2011) · AJP Advances in Physiology Education · doi:10.1152/advan.00002.2011 · Cited by 10
Suki, B. (2010) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01102.2010 · Cited by 10
Defining and Measuring Team Effectiveness in Dynamic Environments and Implications for Team ITS
Gorman, J. C., Grimm, D., & Dunbar, T. A. (2018) · Research on managing groups and teams · doi:10.1108/s1534-085620180000019007 · Cited by 12
de Souza, N. M., Giacon, T. R., Pacagnelli, F. L., Barbosa, M. P. C. R., Valenti, V. E., & Vanderlei, L. C. M. (2016) · Cardiology in the Young · doi:10.1017/s104795111500270x · Cited by 9
Basso, A., & PIANTANELLI, L. (2002) · Experimental Gerontology · doi:10.1016/s0531-5565(02)00126-2 · Cited by 9
Rankin, S. K., & Limb, C. J. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00970 · Cited by 9
Wang, P., Wijnants, M. L., & Ritter, S. M. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01771 · Cited by 9
Yang, H.-W., Garaulet, M., Li, P., Bandín, C., Lin, C., Lo, M., & Hu, K. (2021) · Nutrients · doi:10.3390/nu13072463 · Cited by 8
Favela, L. H., Coey, C. A., Griff, E. R., & Richardson, M. J. (2016) · Neuroscience Letters · doi:10.1016/j.neulet.2016.05.017 · Cited by 12
A Patient Suffering From Neurodegenerative Disease May Have a Strengthened Fractal Gait Rhythm
Ren, H., Yang, Y., Gu, C., Weng, T., & Yang, H. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2860971 · Cited by 9
Koshino, T., Kimura, Y., Kameyama, Y., Takahashi, T., Yasui, T., Chisaka, H., Sugawara, J., & Okamura, K. (2003) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00268.2002 · Cited by 9
Optimizing a Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences
Mangalam, M., Wilson, T. J., Sommerfeld, J. H., & Likens, A. D. (2025) · Axioms · doi:10.3390/axioms14060421 · Cited by 8
Liddy, J. J., Ducharme, S. W., van Emmerik, R. E. A., & Haddad, J. M. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.121784 · Cited by 8
The Value of Heart Rhythm Complexity in Identifying High-Risk Pulmonary Hypertension Patients
Tang, S.-Y., Ma, H., Hung, C., Kuo, P., Lin, C., Lo, M., Hsu, H., Chiu, Y.-W., Tsai, C.-H., Lin, Y.-T., et al. (2021) · Entropy · doi:10.3390/e23060753 · Cited by 8
CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS
Sree, S. V., Ghista, D. N., & Ng, K.-H. (2012) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519412400325 · Cited by 8
The Mismatch of Intrinsic Fluctuations and the Static Assumptions of Linear Statistics
Amon, M. J., & Holden, J. G. (2019) · Review of Philosophy and Psychology · doi:10.1007/s13164-018-0428-x · Cited by 8
The Dynamical Analysis of Log Data Within Educational Games
Snow, E. L., Allen, L. K., & McNamara, D. S. (2015) · Springer eBooks · doi:10.1007/978-3-319-05834-4_4 · Cited by 8
Mangalam, M., & Likens, A. D. (2025) · Entropy · doi:10.3390/e27050500 · Cited by 7
Electrophysiological detection of exam stress in health schools' students
Kazdağlı, H., Özel, H. F., Öztürk, S., Ceylan, D., Erdeniz, B., Özbek, M., & Şemin, M. İ. (2024) · Physiology International · doi:10.1556/2060.2024.00354 · Cited by 7
Lucas-Sánchez, A., Martínez-Nicolás, A., Madrid, J. A., Pagán, P., Mendiola, P., & Costa, J. D. A. (2014) · Chronobiology International · doi:10.3109/07420528.2014.984040 · Cited by 7
Soft-assembled human–machine perceptual systems
Favela, L. H. (2019) · Adaptive Behavior · doi:10.1177/1059712319847129 · Cited by 7
Loewner Equation with Chaotic Driving Function Describes Neurite Outgrowth Mechanism
Shibasaki, Y., & Saito, M. (2019) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.88.063801 · Cited by 7
Can the Detrended Fluctuation Analysis Reveal Nonlinear Components of Heart Rate Variabilityƒ
Castiglioni, P., Parati, G., & Faini, A. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856945 · Cited by 7
Acute High-Intensity Exercise Reduces Performance Anxiety: A Pilot Study in Wind Musicians
Blasco-Lafarga, C., Roldán, A., Cordellat, A., & Monteagudo, P. (2022) · Annals of Applied Sport Science · doi:10.52547/aassjournal.1094 · Cited by 7
Global chaotic parameters of heart rate variability during mental task
Fontes, A. M. G. G., Garner, D. M., de Abreu, L. C., Barbosa, J. C., de Assis, E. V., de Souza, A. C. A., Porto, A. A., & Valenti, V. E. (2015) · Complexity · doi:10.1002/cplx.21656 · Cited by 7
Castiglioni, P., & Rienzo, M. D. (2010) · Computing in cardiology · Cited by 7
A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
Mendez, M. M., Hsu, M.-C., Yuan, J.-T., & Lynn, K. (2022) · Applied Sciences · doi:10.3390/app12052387 · Cited by 7
Castiglioni, P., Zaza, A., Merati, G., & Faini, A. (2025) · Mathematics · doi:10.3390/math13182955 · Cited by 7
Vadrevu, K. P. (2023) · Environmental Research Communications · doi:10.1088/2515-7620/acb041 · Cited by 6
Vasomotion heterogeneity and spectral characteristics in diabetic and hypertensive patients
Zhao, L., Liu, S., Liu, Y., & Tang, H. (2023) · Microvascular Research · doi:10.1016/j.mvr.2023.104620 · Cited by 6
Hegde, V. N., Deekshit, R., & Satyanarayana, P. S. (2013) · doi:10.5121/csit.2013.3103 · Cited by 6
Kas, M. J., Golani, I., Benjamini, Y., Fonio, E., & Stiedl, O. (2011) · Neuromethods · doi:10.1007/978-1-61779-313-4_1 · Cited by 6
Irregular Metronomes as Assistive Devices to Promote Healthy Gait Patterns
Likens, A. D., Mastorakis, S., Σκιαδόπουλος, Α., Kent, J. A., Azad, M. W. A., & Stergiou, N. (2021) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc49032.2021.9369490 · Cited by 6
Assessment of fetal development by HRV and chaotic global techniques
Garner, D. M., Leeuwen, P. V., Grönemeyer, D. H. W., & Moosavi, S. H. (2016) · Journal of Human Growth and Development · doi:10.7322/jhgd.119258 · Cited by 6
Surface electromyography for analysis of heart rate variability in preterm infants
Jost, K., Scherer, S., Angelis, C. D., Büchler, M., Datta, A., Cattin, P. C., Frey, U., Suki, B., & Schulzke, S. M. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa996a · Cited by 6
Servadio, J. L., Machado, G., Álvarez, J., Júnior, F. E. F. D. L., Alves, R. V., & Convertino, M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235920 · Cited by 6
DePetrillo, P. B., Bennett, A. J., Speers, D., Suomi, S. J., Shoaf, S. E., Karimullah, K., & Higley, J. D. (2000) · European Journal of Pharmacology · doi:10.1016/s0014-2999(00)00056-x · Cited by 6
Nonlinear dynamics in heart rate variability
Beckers, F., Ramaekers, D., & AUBERT, A. E. (2002) · Computers in cardiology · doi:10.1109/cic.2000.898473 · Cited by 6
Aging and Slowing of the Neuromotor System
Morrison, S., & Newell, K. M. (2017) · Springer eBooks · doi:10.1007/978-981-287-082-7_230 · Cited by 6
Yin-yang in modern traditional Chinese medicine: From mechanisms to digital innovation
Yang, G.-M., Pu, T., Tao, F., Quan, X., & Cheng, K. F. (2025) · Journal of Traditional Chinese Medical Sciences · doi:10.1016/j.jtcms.2025.09.005 · Cited by 5
Churchill, N. W., Roudaia, E., Chen, J. J., Gilboa, A., Sekuler, A. B., Ji, X., Gao, F., Lin, Z., Masellis, M., Goubran, M., et al. (2023) · Brain and Behavior · doi:10.1002/brb3.3212 · Cited by 5
Multifractal Multiscale Analysis of Human Movements during Cognitive Tasks
Faini, A., Arsac, L. M., Deschodt‐Arsac, V., & Castiglioni, P. (2024) · Entropy · doi:10.3390/e26020148 · Cited by 5
Fractal characterization of acupuncture-induced spike trains of rat WDR neurons
Chen, Y., Guo, Y., Wang, J., Hong, S., Wei, X., Yu, H., & Deng, B. (2015) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2015.05.026 · Cited by 5
Khan, A. A. S., Mumtahina, U., & Yeasmin, N. (2013) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/iciev.2013.6572679 · Cited by 5
Stress during pregnancy: Is the autonomic nervous system influenced by anxiety?
Taelman, J., Vandeput, S., Widjaja, D., Braeken, M. A. K. A., Otte, R. A., den Bergh, B. R. H. V., & Huffel, S. V. (2010) · Research portal (Tilburg University) · Cited by 5
Non-linear dynamics of heart rate variability in athletes: effect of training
AUBERT, A. E., Beckers, F., & Seps, B. (2003) · Computers in cardiology · doi:10.1109/cic.2002.1166804 · Cited by 5
Günaydın, B., & İkizoğlu, S. (2023) · Biomedical Engineering Letters · doi:10.1007/s13534-023-00285-9 · Cited by 4
Abduction and Deduction in Dynamical Cognitive Science
Chemero, A. P. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12692 · Cited by 4
Castiglioni, P. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35686-5_14 · Cited by 4
Mongue‐Din, H., Salmon, A., Fiszman, M. Y., & Fromes, Y. (2008) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.90376.2008 · Cited by 4
Byrd, C. J., McConn, B. R., Gaskill, B. N., Schinckel, A. P., Green-Miller, A. R., Lay, D. C., & Johnson, J. S. (2022) · Journal of Animal Science · doi:10.1093/jas/skac004 · Cited by 5
Gender-Related Differences In Nonlinear Indices Of Heart Rate Variability
Beckers, F., Ramaekers, D., & AUBERT, A. E. (2001) · Cited by 4
Nonlinear measures of heart beat intervals differ in female patients with chest pain
Yamada, M. G., Yamashita, T., Oda, Y., Emura, S., Yamashiro, S., & Koizumi, S. (2004) · Nonlinear Analysis Real World Applications · doi:10.1016/j.nonrwa.2004.08.004 · Cited by 4
AUTOMATED DIAGNOSIS OF CARDIAC HEALTH USING RECURRENCE QUANTIFICATION ANALYSIS
Krishnan, M. M. R., Sree, S. V., Ghista, D. N., Ng, E. Y. K., Swapna, ANG, A. P. C., Ng, K.-H., & Suri, J. S. (2012) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519412400143 · Cited by 4
Taffe, L. R. (2016) · JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH · doi:10.7860/jcdr/2016/20410.8343 · Cited by 4
Effect of Spinal Cord Injury on Nonlinear Complexity of Skin Blood Flow Oscillations
Jan, Y., Liao, F., & Burns, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21657-2_37 · Cited by 4
Continuous Glucose Monitoring and Tight Glycaemic Control in Critically Ill Patients
Signal, M. (2013) · UC Research Repository (University of Canterbury) · doi:10.26021/3559 · Cited by 4
Aging and Slowing of the Neuromotor System
Morrison, S., & Newell, K. M. (2016) · Springer eBooks · doi:10.1007/978-981-287-080-3_230-1 · Cited by 4
Castiglioni, P., Brambilla, V., Brambilla, L., Gualerzi, M., Lazzeroni, D., & Coruzzi, P. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318357 · Cited by 3
Advances in Multivariate and Multiscale Physiological Signal Analysis
Lanatà, A., & Nardelli, M. (2022) · Bioengineering · doi:10.3390/bioengineering9120814 · Cited by 3
Martínez, C. A. G., Otero, A., Félix, P., Presedo, J., & Márquez, D. G. (2018) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2018.04.002 · Cited by 3
Shibasaki, Y., Maeda, N., Oshimi, C., Shirakawa, Y., & Saito, M. (2021) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0048559 · Cited by 3
Bahramizadeh-Sajadi, S., Katoozian, H. R., Mehrabbeik, M., Baradaran‐Rafii, A., Jadidi, K., & Jafari, S. (2022) · Fractal and Fractional · doi:10.3390/fractalfract6110688 · Cited by 3
Parameter estimation in Manneville–Pomeau processes
Olbermann, B. P., Faculdade de Matemática-PUCRS, 9.-0. P. A., Lopes, S. R. C., Lopes, A. O., & Instituto de Matemática e Estatística-UFRGS, 9.-9. P. A. (2023) · Probability Uncertainty and Quantitative Risk · doi:10.3934/puqr.2023009 · Cited by 3
Fall risks assessment among community dwelling elderly using wearable wireless sensors
Lockhart, T. E., Soangra, R., & Frames, C. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2050995 · Cited by 3
Effect of dietary omega-3 fatty acid deficiency on heart rate variability in hooded rats
Weisinger, H. S., Salem, N., Makino, K. K., Hibbeln, J. R., Sinclair, A. J., Weisinger, R. S., & DePetrillo, P. B. (2007) · Deakin Research Online (Deakin University) · Cited by 3
Review: Nonlinear Techniques for Analysis of Heart Rate Variability
Tayel, M. B., & AlSaba, E. I. (2017) · WSEAS Transactions on Signal Processing archive · Cited by 3
Serletis, D. (2010) · TSpace (University of Toronto) · Cited by 3
Pacing to pink noise enhances gait recovery from a mechanical perturbation
Manifrenti, M., Kent, J. A., Wickstrom, J., Stergiou, N., & Likens, A. D. (2025) · Journal of Experimental Biology · doi:10.1242/jeb.250650 · Cited by 2
Comparison of Machine Learning Models in Nonlinear and Stochastic Signal Classification
Olejarczyk, E., & Massaroni, C. (2025) · Applied Sciences · doi:10.3390/app152011226 · Cited by 2
Gait variability is more strongly associated with walking speed than with age
Bowersock, C. D., Szklo‐Coxe, M., Schussler, E., Morrison, S., & Russell, D. M. (2025) · Gait & Posture · doi:10.1016/j.gaitpost.2025.110086 · Cited by 2
Neurocardiac diving reflex dynamics: assessment and prediction
Malinowski, K. S., Pawłowski, R., & Winklewski, P. J. (2026) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-026-02187-0 · Cited by 2
Emotions and Mood States: Modeling, Elicitation, and Classification
Valenza, G., & Scilingo, E. P. (2013) · Series in bioengineering · doi:10.1007/978-3-319-02639-8_2 · Cited by 2
Dynamical Aspects of Pharmacokinetic/Pharmacodynamic & Quantitative Systems Pharmacology Models
Loisios-Konstantinidis, I., Mavroudis, P. D., & Macheras, P. E. (2020) · Human perspectives in health sciences and technology · doi:10.1007/978-3-030-32857-3_2 · Cited by 2
Characterization of complex data functions through local persistence of increments
Barros, M. M., Venturelli, F., & Bevilácqua, L. (2020) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2020.105590 · Cited by 2
Multifractal spatiotemporal dynamics in human epileptiform stereoelectroencephalography recordings
John, N. S., Bulacio, J. C., Alexopoulos, A. V., Bingaman, W. E., Najm, I., Krishnan, B., & Serletis, D. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adf66a · Cited by 2
Facultad de Ciencias, U. A. D. S. L. P., Salgado, R. C., Méndez, M. O., Laboratorio Nacional CI3M, F. D. C., Murguía, J. S., Laboratorio Nacional CI3M, F. D. C., Cárdenas, S., Facultad de Ciencias, U. A. D. S. L. P., Agundis, B. A., Facultad de Ciencias, U. A. D. S. L. P., et al. (2018) · Memorias del Congreso Nacional de Ingeniería Biomédica · doi:10.24254/cnib.18.36 · Cited by 2
Castiglioni, P., Parati, G., & Faini, A. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629623 · Cited by 2
Null or Linear-Phase Filters for the Derivation of a New Variant of the MSE
Grivel, É., Berthelot, B., Legrand, P., & Colin, G. (2023) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco58844.2023.10289829 · Cited by 2
Mikkelsen, K. B., & Lund, T. E. (2013) · Frontiers in Physiology · doi:10.3389/fphys.2013.00106 · Cited by 2
Tsuji, Y., Asakawa, T., Hitomi, Y., Todo, A., Yoshida, T., & Mizuno‐Matsumoto, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02753-1_22 · Cited by 2
Statistical Modeling of Electrocardiography Signal for Subject Monitoring and Diagnosis
Chen, L., Song, C., & Zhang, X. (2016) · John Wiley & Sons, Inc. eBooks · doi:10.1002/9781118919408.ch4 · Cited by 2
Modelling the stock market using a multi-scale approach
(49953), F. L. (2017) · Figshare · Cited by 2
You, W., & Limperopoulos, C. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/210518 · Cited by 2
Assessing the Exacerbations Risk of Influenza-Associated Chronic Occupational Asthma
Liao, C., Hsieh, N., Chio, C., & Chen, S. (2010) · Risk Analysis · doi:10.1111/j.1539-6924.2010.01402.x · Cited by 1
Investigations of cardiac rhythm fluctuation using the DFA method
Slabu, L., Negoiţă, C., Sandu, A., Amariei, L., Corduneanu, C., & Grosu, I. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2001.1018985 · Cited by 1
Gagnon, K. T. (2016) · J. Willard Marriott Library · doi:10.26053/0h-2fbe-7000 · Cited by 1
Gao, J., Gu, C., Yang, H., & Shen, C. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113980 · Cited by 1
Metadata
- Authors
- Peng, C.-K., Havlin, S., Hausdorff, J. M., Mietus, J. E., Stanley, H. E., & Goldberger, A. L.
- Affiliations
- Boston University (US)
- Harvard University (US)
- Corresponding author
- Peng, C.-K.
- Venue
- Journal of Electrocardiology
- Publisher
- Elsevier BV
- Published
- 1995
- Volume
- 28
- Pages
- 59-65
- ISSN
- 0022-0736 (ISSN-L)
- OpenAlex
- W1824152488
- Cited by
- 358
- References
- 30
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 7 · 2023: 18 · 2022: 9 · 2021: 18 · 2020: 16 · 2019: 27 · 2018: 15 · 2017: 19 · 2016: 20 · 2015: 16 · 2014: 25 · 2013: 27 · 2012: 19
- FWCI
- 0.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Electrocardiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- National Aeronautics and Space Administration
- National Institutes of Health
- National Institute on Aging
- National Institute of Mental Health
- National Institute on Drug Abuse
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- heart rate dynamics, fractal scaling, long-range correlations, power-law correlations, cardiac interbeat intervals, cardiac autonomic regulation, congestive heart failure, sudden cardiac death risk, cardiac risk assessment
- MeSH terms
- Adult, Aged, Algorithms, Electrocardiography, Forecasting, Heart Diseases, Heart Failure, Heart Rate, Humans, Middle Aged, Models, Cardiovascular, Risk Factors, Survival Rate, Case-Control Studies, Death, Sudden, Cardiac, Fractals, Ventricular Dysfunction, Left, Risk Assessment
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