Linear and nonlinear indices of variability in respiratory movement, the electroencephalogram and the electrocardiogram

Using respiratory movement and electroencephalographic (EEG) signals from clinically healthy subjects, methods of deterministic chaos are illustrated. In particular, the correlation dimensions (D2) and the approximate entropy (ApEn) are computed. Their interpretation as a way to assess nonlinear dynamics is discussed in the light of analyses on surrogate data series. As separate, yet complementary elements of chronomes (time structures), their interrelations with rhythms are illustrated for the case of electrocardiographic (ECG) data in health and coronary artery disease. The role of chronomes in natural environmental variables as synchronizers of physiological chronomes is illustrated for the case of magnetic pulsations.