Estimation of the Uncertainty in Time Domain Indices of RR Time Series

A method for estimating the uncertainty in time-domain indices of RR time series is described. The method relies on the central limit theorem that states that the distribution of a sample average of independent samples has an uncertainty that asymptotically approaches to the sample standard deviation divided by the square root of the number of samples. Because RR time series cannot be characterized by a set of independent samples, we propose to estimate the uncertainty of indices by computing them in blocks that satisfy that the obtained partial indices are independent. We propose a methodology to search sets of independent partial indices and apply this methodology to the estimation of the uncertainty in the mean RR, SDRR, and r-msDD indices. The results show that the uncertainty can be higher than the 10% of the index for the SDRR and even higher for the r-msDD. Moreover, a statistical test for the difference of two indices is proposed

[1]  Variations in breathing patterns increase low frequency contents in HRV spectra. , 2000, Physiological measurement.

[2]  Dennis J. McFarland,et al.  Design and operation of an EEG-based brain-computer interface with digital signal processing technology , 1997 .

[3]  S. Standard.  GUIDE TO THE EXPRESSION OF UNCERTAINTY IN MEASUREMENT , 2006 .

[4]  Jeffrey M. Hausdorff,et al.  Long-range anticorrelations and non-Gaussian behavior of the heartbeat. , 1993, Physical review letters.

[5]  K.-R. Muller,et al.  Linear and nonlinear methods for brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[7]  A. Aubert,et al.  Heart Rate Variability in Athletes , 2003, Sports medicine.

[8]  Pekka Raatikainen,et al.  Prediction of sudden cardiac death after myocardial infarction in the beta-blocking era. , 2003, Journal of the American College of Cardiology.

[9]  H. Künsch.  The Jackknife and the Bootstrap for General Stationary Observations , 1989 .

[10]  D. Levy,et al.  Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study. , 1996, Circulation.

[11]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[12]  G Pfurtscheller,et al.  Adaptive Autoregressive Modeling used for Single-trial EEG Classification - Verwendung eines Adaptiven Autoregressiven Modells für die Klassifikation von Einzeltrial-EEG-Daten , 1997, Biomedizinische Technik. Biomedical engineering.

[13]  E. Migliaro,et al.  Heart Rate Variability: Short‐Term Studies are as Useful as Holter to Differentiate Diabetic Patients from Healthy Subjects , 2003, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[14]  Jukka Heikkonen,et al.  Sequential Classification of Finger Movements from MEG Recordings , 2004 .

[15]  Stephen J. Roberts,et al.  Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation , 2004, IEEE Transactions on Biomedical Engineering.

[16]  J. Fleiss,et al.  The ability of several short-term measures of RR variability to predict mortality after myocardial infarction. , 1993, Circulation.

[17]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[18]  G. Pfurtscheller,et al.  Graz-BCI: state of the art and clinical applications , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  R. Prescott,et al.  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 (UK-heart). , 1998, Circulation.

[20]  W. Wood,et al.  Physical activity, mental stress, and short-term heart rate variability in patients with ischemic heart disease. , 1998, Journal of cardiopulmonary rehabilitation.

[21]  G. Forrester,et al.  Robustness of the t and U tests under combined assumption violations , 1998 .

[22]  G. Pfurtscheller,et al.  Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[23]  G. Pfurtscheller,et al.  Rapid prototyping of an EEG-based brain-computer interface (BCI) , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  Julius S. Bendat,et al.  Random Data - Analysis and Measurement Procedures - Second Edition (revised and expanded) , 1986 .

[25]  G Pfurtscheller,et al.  Current trends in Graz Brain-Computer Interface (BCI) research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[26]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .