Natural entropy fluctuations discriminate similar-looking electric signals emitted from systems of different dynamics.

Complexity measures are introduced that quantify the change of the natural entropy fluctuations at different length scales in time series emitted from systems operating far from equilibrium. They identify impending sudden cardiac death (SD) by analyzing 15 min electrocardiograms, and comparing to those of truly healthy humans (H). These measures seem to be complementary to the ones suggested recently [Phys. Rev. E 70, 011106 (2004)]] and altogether enable the classification of individuals into three categories: H, heart disease patients, and SD. All the SD individuals, who exhibit critical dynamics, result in a common behavior.

[1]  P. Laguna,et al.  New algorithm for QT interval analysis in 24-hour Holter ECG: performance and applications , 2006, Medical and Biological Engineering and Computing.

[2]  P. Varotsos,et al.  Entropy in the natural time domain. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  M. Bray Visualization and analysis of electrodynamic behavior during cardiac arrhythmias , 2003 .

[4]  P. Varotsos,et al.  Electric fields that "arrive" before the time derivative of the magnetic field prior to major earthquakes. , 2003, Physical review letters.

[5]  P. Varotsos,et al.  Attempt to distinguish electric signals of a dichotomous nature. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  George Manis,et al.  Experimental analysis of heart rate variability of long-recording electrocardiograms in normal subjects and patients with coronary artery disease and normal left ventricular function , 2003, J. Biomed. Informatics.

[7]  P. Varotsos,et al.  Long-range correlations in the electric signals that precede rupture: further investigations. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Dante R. Chialvo,et al.  Physiology: Unhealthy surprises , 2002, Nature.

[9]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[10]  P. Varotsos,et al.  Long-range correlations in the electric signals that precede rupture. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  H. Tanaka,et al.  Electric and magnetic phenomena observed before the volcano-seismic activity in 2000 in the Izu Island Region, Japan , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[12]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[13]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[14]  I. Khan Long QT syndrome: diagnosis and management. , 2002, American heart journal.

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

[16]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[17]  S. Havlin,et al.  Scale-specific and scale-independent measures of heart rate variability as risk indicators , 1999, physics/9909029.

[18]  P Caminal,et al.  Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. , 1994, Computers and biomedical research, an international journal.

[19]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[20]  P. Varotsos,et al.  Earthquake prediction and electric signals , 1986, Nature.

[21]  Ralph O. Simmons,et al.  Thermodynamics of Point Defects and Their Relation With Bulk Properties , 1985 .