Analysis of heart rate variability as a predictor of mortality in cardiovascular patients of intensive care unit

Marshall, 2001, From Celsus to Galen to bone: the illnesses, syndromes, and diseases of acute inflammation, 3 Kennedy, 1998, Heart rate variability—a potential, noninvasive prognostic index in the critically ill patient, Crit Care Med, 26, 213, 10.1097/00003246-199802000-00010 Seely, 2000, Multiple organ dysfunction syndrome: exploring the paradigm of complex nonlinear systems, Crit Care Med, 28, 2193, 10.1097/00003246-200007000-00003 Clermont, 2004, Towards understanding pathophysiology in critical care: the human body as a complex system, 13 Sprung, 2003, End-of-life practices in European intensive care units: the Ethicus study, JAMA, 290, 790, 10.1001/jama.290.6.790 Ferrand, 2001, Withholding and withdrawal of life support in intensive-care units in France: a prospective survey, Lancet, 357, 9, 10.1016/S0140-6736(00)03564-9 Esteban, 2001, Withdrawing and withholding life support in the intensive care unit: a Spanish prospective multi-centre observational study, Intensive Care Med, 27, 1744, 10.1007/s00134-001-1111-7 McLean, 2000, Death in two Canadian intensive care units: institutional difference and changes over time, Crit Care Med, 28, 100, 10.1097/00003246-200001000-00016 Wunsch, 2005, End-of-life decisions: a cohort study of the withdrawal of all active treatment in intensive care units in the United Kingdom, Intensive Care Med, 31, 823, 10.1007/s00134-005-2644-y Yazigi, 2005, Withholding and withdrawal of life sustaining treatment in a Lebanese intensive care unit: a prospective observational study, Intensive Care Med, 31, 562, 10.1007/s00134-005-2578-4 Buckley, 2004, Limitation of life support: frequency and practice in a Hong Kong intensive care unit, Crit Care Med, 32, 415, 10.1097/01.CCM.0000110675.34569.A9 Azoulay, 2009, SAPS 3 investigators: end-of-life practices in 282 intensive care units: data from the SAPS 3 database, Intensive Care Med, 35, 623, 10.1007/s00134-008-1310-6 Sprung, 2007, The importance of religious affiliation and culture on end-of-life decisions in European intensive care units, Intensive Care Med, 33, 1732, 10.1007/s00134-007-0693-0 Moselli, 2006, Forgoing life sustaining treatments: differences and similarities between North America and Europe, Acta Anaesthesiol Scand, 50, 1177, 10.1111/j.1399-6576.2006.01150.x Ramon, 2007, Mining data from intensive care patients, Adv Eng Inform, 21, 243, 10.1016/j.aei.2006.12.002 Silva, 2006, Mortality assessment in intensive care units via adverse events using artificial neural networks, Artif Intell Med, 36, 223, 10.1016/j.artmed.2005.07.006 Rosenberg, 2002, Recent innovations in intensive care unit risk-prediction models, Curr Opin Crit Care, 8, 321, 10.1097/00075198-200208000-00009 Knaus, 2002, APACHE 1978–2001: the development of a quality assurance system based on prognosis: milestones and personal reflections, Arch Surg, 137, 37, 10.1001/archsurg.137.1.37 Knaus, 1991, The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults, Chest, 100, 1619, 10.1378/chest.100.6.1619 Le Gall, 1984, A simplified acute physiology score for ICU patients, Crit Care Med, 12, 975, 10.1097/00003246-198411000-00012 Le Gall, 1993, A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study, JAMA, 270, 2957, 10.1001/jama.1993.03510240069035 Morik, 2000, Knowledge discovery and knowledge validation in intensive care, Artif Intell Med, 19, 225, 10.1016/S0933-3657(00)00047-6 Moser, 1999, Application of data mining to intensive care unit microbiologic data, Emerg Infect Dis, 5, 454, 10.3201/eid0503.990320 Ganzert, 2002, Analysis of respiratory pressure volume curves in intensive care medicine using inductive machine learning, Artif Intell Med, 26, 69, 10.1016/S0933-3657(02)00053-2 Lucas, 2004, Bayesian analysis, pattern analysis, and data mining in health care, Curr Opin Crit Care, 10, 399, 10.1097/01.ccx.0000141546.74590.d6 Kreke, 2004, Simulation and critical care modeling, Curr Opin Crit Care, 10, 395, 10.1097/01.ccx.0000139361.30327.20 Kong, 2004, Advances in statistical methodology and their application in critical care, Curr Opin Crit Care, 10, 391, 10.1097/01.ccx.0000140940.96505.71 Sierra, 2001, Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data, Artif Intell Med, 22, 233, 10.1016/S0933-3657(00)00111-1 Garrard, 1993, Spectral analysis of heart rate variability in sepsis syndrome, Clin Auton Res, 3, 5, 10.1007/BF01819137 Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996, Standards of measurement, physiological interpretation and clinical use, Circulation, 93, 1043, 10.1161/01.CIR.93.5.1043 Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996, Standards of measurement, physiological interpretation and clinical use, Eur Heart J, 75, 354 Akselroad, 1981, Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control, Science, 213, 220, 10.1126/science.6166045 Tsuji, 1994, Reduced heart rate variability and mortality risk in an elderly cohort: the Framingham Heart Study, Circulation, 90, 878, 10.1161/01.CIR.90.2.878 Son, 2010, Application of support vector machine for prediction of medication adherence in heart failure patients, Healthc Inform Res, 16, 253, 10.4258/hir.2010.16.4.253 Hamadene, 2006, Interpretation of RQA variables: Application to the prediction of epileptic seizures, 16 http://www.uptodate.com. Xing, 2012, Non-sinus rhythm after heart surgery: permanent or not? A simple method can tell, Asian Cardiovasc Thoracic Ann, 20, 495, 10.1177/0218492312452267 Feng, 2011, An efficient method of addressing ectopic beats: new insight into data preprocessing of heart rate variability, J Zhejiang Univ-Sci B (Biomed Biotechnol), 12, 976, 10.1631/jzus.B1000392 MIMIC II Database. http://physionet.org/physiobank/database/mimic2db/. Mazzeo, 2011, Heart rate variability: a diagnostic and prognostic tool in anesthesia and intensive care, Acta Anaesthesiol Scand, 55, 797, 10.1111/j.1399-6576.2011.02466.x Brennan, 2001, Do existing measures of poincare plot geometry reflect nonlinear features of heart rate variability, IEEE Trans Biomed Eng, 48, 1342, 10.1109/10.959330 Marwan N. Encounters with neighbours: current developments of concepts based on recurrence plots and their applications, Potsdam, 2003. 331 s Dizertačni prace. University of Potsdam, 2003. Dostupne z http://www.recurrenceplot.tk/furtherreading.php. ISBN 3000123474, 9783000123474. Kennel, 1992, Determining embedding dimension for phase-space reconstruction using a geometrical construction, Phys Rev A, 45, 3403, 10.1103/PhysRevA.45.3403 Fraser, 1986, Independent coordinates for strange attractors from mutual information, Phys Rev A, 33, 1134, 10.1103/PhysRevA.33.1134 Zbilut, 1992, Embeddings and delays as derived from quantification of recurrence plots, Phys Lett A, 171, 199, 10.1016/0375-9601(92)90426-M Marwan, 2007, Recurrence plots for the analysis of complex systems, Phys Rep, 438, 237, 10.1016/j.physrep.2006.11.001 Matassini, 2002, Optimizing of recurrence plots for noise reduction, Phys Rev E Stat Nonlinear Soft Matter Phys, 65, 021102, 10.1103/PhysRevE.65.021102 Thiel, 2002, Influence of observational noise on the recurrence quantification analysis, Physica D, 171, 138, 10.1016/S0167-2789(02)00586-9 Eckmann, 1987, Recurrence plots of dynamical systems, Europhys Lett, 4, 973, 10.1209/0295-5075/4/9/004 Tsuji, 1994, Reduced heart rate variability and mortality risk in an elderly cohort: the Framingham Heart Study, Circulation, 90, 878, 10.1161/01.CIR.90.2.878 Jacqueline, 2000, Low heart rate variability in a 2-minute rhythm strip predicts risk of coronary heart disease and mortality from several causes, Circulation, 102, 1239, 10.1161/01.CIR.102.11.1239