Analysis of Heart Beat Dynamics through Singularity Spectrum

The analysis to detect arrhythmias and life-threatening conditions are highly essential in today world and this analysis can be accomplished by advanced non-linear processing methods for accurate analysis of the complex signals of heartbeat dynamics. In this perspective, recent developments in the field of multiscale information content have lead to the Microcanonical Multiscale Formalism (MMF). We show that such framework provides several signal analysis techniques that are especially adapted to the study of heartbeat dynamics. In this paper, we just show first hand results of whether the considered heartbeat dynamics signals have the multiscale properties by computing local preticability exponents (LPEs) and the Unpredictable Points Manifold (UPM), and thereby computing the singularity spectrum. Keywords—Microcanonical Multiscale Formalism (MMF), Unpredictable Points Manifold (UPM), Heartbeat Dynamics.

[1]  Richard G. Baraniuk,et al.  Multiscale queuing analysis of long-range-dependent network traffic , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[2]  H. Stanley,et al.  Behavioral-independent features of complex heartbeat dynamics. , 2001, Physical review letters.

[3]  Antonio Turiel,et al.  Application of the microcanonical multifractal formalism to monofractal systems. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Stéphane Jaffard,et al.  Multifractal formalism for functions part II: self-similar functions , 1997 .

[5]  H. Stanley,et al.  Magnitude and sign correlations in heartbeat fluctuations. , 2000, Physical review letters.

[6]  Antonio Turiel,et al.  Microcanonical multifractal formalism—a geometrical approach to multifractal systems: Part I. Singularity analysis , 2008 .

[7]  Luís A. Nunes Amaral,et al.  From 1/f noise to multifractal cascades in heartbeat dynamics. , 2001, Chaos.

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

[9]  H. Stanley,et al.  Scale invariance in the nonstationarity of human heart rate. , 2000, Physical review letters.

[10]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[11]  Antonio Turiel,et al.  Numerical methods for the estimation of multifractal singularity spectra on sampled data: A comparative study , 2006, J. Comput. Phys..

[12]  Plamen Ch. Ivanov,et al.  Long-Range Dependence in Heartbeat Dynamics , 2003 .

[13]  Antonio Turiel,et al.  On Optimal Wavelet Bases for the Realization of Microcanonical Cascade Processes , 2008, Int. J. Wavelets Multiresolution Inf. Process..

[14]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[15]  Kenneth Falconer,et al.  Fractal Geometry: Mathematical Foundations and Applications , 1990 .

[16]  I. Simonsen,et al.  Determination of the Hurst exponent by use of wavelet transforms , 1997, cond-mat/9707153.

[17]  Stéphane Jaffard,et al.  Multifractal formalism for functions part I: results valid for all functions , 1997 .

[18]  Oriol Pont,et al.  An Optimized Algorithm for the Evaluation of Local Singularity Exponents in Digital Signals , 2011, IWCIA.

[19]  R. Klabunde.  Cardiovascular Physiology Concepts , 2021 .

[20]  Samuel Peter Kozaitis.  Improved feature detection in ECG signals through denoising , 2008 .

[21]  C L Jonesyxk.  Wavelet packet computation of the Hurst exponent , 1996 .

[22]  L. Amaral,et al.  Multifractality in human heartbeat dynamics , 1998, Nature.

[23]  S. Thurner,et al.  Multiresolution Wavelet Analysis of Heartbeat Intervals Discriminates Healthy Patients from Those with Cardiac Pathology , 1997, adap-org/9711003.

[24]  N. Parga,et al.  Ju l 2 00 1 The multi-fractal structure of contrast changes in natural images : from sharp edges to textures , 2008 .