Loading, Plotting, and Filtering RR Intervals

The initial steps to work with RHRV functions are presented in this chapter. The process starts with the loading of records containing beat positions that should be preprocessed prior to frequency, time, or nonlinear analysis. Data can be stored in various types of files, and RHRV routines can deal with different data formats. Next, heart rate must be obtained from beat positions. It may occur that spurious points appear in the heart rate signal. RHRV allows users to delete these outliers, when necessary. Besides, the signal can be filtered to reject automatically points that do not correspond to acceptable physiological values.

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

[2]  Liu Hongxing,et al.  Heart rate estimation by iterative Fourier interpolation algorithm , 2014 .

[3]  M. J. Goldman.  Principles of Clinical Electrocardiography , 1973 .

[4]  A. Camm,et al.  Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques. , 1989, European heart journal.

[5]  J. Fleiss,et al.  Frequency Domain Measures of Heart Period Variability and Mortality After Myocardial Infarction , 1992, Circulation.

[6]  Antonio Molina–Picó,et al.  Influence of QRS complex detection errors on entropy algorithms. Application to heart rate variability discrimination , 2013, Comput. Methods Programs Biomed..

[7]  Leandro Rodríguez Liñares,et al.  VARVI: A software tool for analyzing the variability of the heart rate in response to visual stimuli , 2013, Computing in Cardiology 2013.

[8]  R. A. Thuraisingham,et al.  Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals , 2006, Comput. Methods Programs Biomed..

[9]  Bob Kemp,et al.  European data format ‘plus’ (EDF+), an EDF alike standard format for the exchange of physiological data , 2003, Clinical Neurophysiology.

[10]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[11]  M. Aboy,et al.  Impulse rejection filter for artifact removal in spectral analysis of biomedical signals , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Mobyen Uddin Ahmed,et al.  K-NN based interpolation to handle artifacts for heart rate variability analysis , 2011, 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[13]  S. Barro,et al.  Time-frequency analysis of heart-rate variability , 1997, IEEE Engineering in Medicine and Biology Magazine.

[14]  Mika P. Tarvainen,et al.  Software for advanced HRV analysis , 2004, Comput. Methods Programs Biomed..

[15]  A Värri,et al.  A simple format for exchange of digitized polygraphic recordings. , 1992, Electroencephalography and clinical neurophysiology.

[16]  Abdulmotaleb El-Saddik,et al.  A Pattern-Based Windowed Impulse Rejection Filter for Nonpathological HRV Artifacts Correction , 2015, IEEE Transactions on Instrumentation and Measurement.

[17]  Adel Belouchrani,et al.  QRS detection based on wavelet coefficients , 2012, Comput. Methods Programs Biomed..

[18]  F. Alonso-Atienza,et al.  Denoising of Heart Rate Variability signals during tilt test using independent component analysis and multidimensional recordings , 2007, 2007 Computers in Cardiology.

[19]  G. Moody,et al.  The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. , 1992, European heart journal.