Determination of the exact value of the regularization parameter in smoothness priors method with respect to the corresponding cut-off frequencies for designing filters

A filter is an electrical network or software that alters the amplitude and/or phase characteristics of a signal with respect to frequency. Recently, a new detrending method has been presented to remove the slow nonstationary trends from biomedical signals, which is equivalent to high-pass filtering that removes very low frequency components from the given signal. Although many recently published papers, related to the analysis of biomedical signals like the heart rate variability signal, have used the smoothness priors detrending method, there is no given exact relationship between the regularization parameter and the cut-off frequency of the corresponding high pass filter. In this study, we present this relationship by an empirical formula which would allow the researchers to calculate the parameter from the desired frequency response for not only a high pass filter but also other filter types.

[1]  L H Carney,et al.  Time and frequency domain methods for heart rate variability analysis: a methodological comparison , 1994, Computers in Cardiology 1994.

[2]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[3]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[4]  J. Cacioppo,et al.  Principles of psychophysiology : physical, social, and inferential elements , 1990 .

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

[6]  Mika P. Tarvainen,et al.  An advanced detrending method with application to HRV analysis , 2002, IEEE Transactions on Biomedical Engineering.

[7]  Y. Isler,et al.  Heart rate normalization in the analysis of heart rate variability in congestive heart failure , 2010, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[8]  A Basic Introduction to Filters - Active, Passive and Switched-Capacitor , 1995 .

[9]  Willis J. Tompkins,et al.  Biomedical Digital Signal Processing: C Language Examples and Laboratory Experiments for the IBM PC , 1993 .

[10]  Yalcin Isler,et al.  Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure , 2007, Comput. Biol. Medicine.

[11]  C. Yoo,et al.  Effects of detrending for analysis of heart rate variability and applications to the estimation of depth of anesthesia , 2004 .

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

[13]  John G. Proakis,et al.  Digital Signal Processing Using MATLAB , 1999 .

[14]  S. Porges,et al.  The analysis of periodic processes in psychophysiological research , 1989 .