Levels of complexity in scale-invariant neural signals

Abstract

Many physical and physiological signals exhibit complex scale-invariant features characterized by 1/f scaling and long-range power-law correlations, suggesting a possibly common control mechanism. Specifically, it has been suggested that dynamical processes influenced by inputs and feedback on multiple time scales may be sufficient to give rise to 1/f scaling and scale invariance. Two examples of physiologic signals that are the output of hierarchical, multi-scale physiologic systems under neural control are the human heartbeat and human gait. Here we show that while both cardiac interbeat interval and gait interstride interval time series under healthy conditions have comparable 1/f scaling, they still may belong to different complexity classes. Our analysis of the magnitude series correlations and multifractal scaling exponents of the fluctuations in these two signals demonstrates that in contrast with the nonlinear multifractal behavior found in healthy heartbeat dynamics, gait time series exhibit less complex, close to monofractal behavior and a low degree of nonlinearity. These findings underscore the limitations of traditional two-point correlation methods in fully characterizing physiologic and physical dynamics. In addition, these results suggest that different mechanisms of control may be responsible for varying levels of complexity observed in physiological systems under neural regulation and in physical systems that possess similar 1/f scaling.

Gait dynamics in Parkinson’s disease: Common and distinct behavior among stride length, gait variability, and fractal-like scaling

“…Many different aspects of gait could be examined with a dynamical approach using the techniques described as well as a long list of other analysis methods. 38,53,74,152 Hopefully, the present review and update will motivate additional studies and analyses of gait in PD. The results of this update demonstrate that models that attempt to simulate and mimic the changes in gait in PD should also consider the changes in the fractal scaling and the effects of different interventions on multiple gait characteristics.…”

“…Similarly, DFA scaling of the stride time is relatively independent of the data collection method. Ivanov et al 74 recently reported that DFA derived scaling indices of gait obtained using an accelerometer when healthy adults walk on a very long straight path are similar to those of healthy subjects who walked on an oval track while wearing footswitches. 79 Similar scaling values were found by West and Griffin 109 even though another experimental method was used ͑i.e., timing based on knee angles͒, the walking track was a large square path, and the analytical approach used was different.…”

“…Two time series can have identical means and variance but with very different ordering or dynamics ͑e.g., see http:// www.physionet.org/tutorials/ndc/͒. There are many ways to capture and quantify such properties; 38,42,46,53,[68][69][70][71][72][73][74][75][76] however, in this review, we focus on the fractal property of gait.…”

Tonic and transient oscillatory brain activity during acute exercise

“…Right panel shows lower beta power across time at the parieto-occipital cluster (8 electrodes) in standard trials. (C) Theta frequency band [4][5][6][7][8] parametric paired t-test maps comparing moderate-to-high intensity and light intensity exercise in target 2 trials (red squares). Right panel shows theta power across time at the globally-localized cluster (7 electrodes) in target 2 trials.…”

Development of the ‘Healthcor’ System as a Cardiac Disorders Symptoms Detector using an Expert System based on Arduino Uno

“…A representation that relates to time versus frequency information of the signal can be achieved using wavelets; this works well on nonstationary data (de Chazal et al, 2000;Minami et al, 1999;Romero & Serrano, 2001, Sarkaleh & Shahbahrami, 2012. Some algorithms have utilized heartbeat temporal intervals (Alexakis et al, 2003), morphological features (de Chazal et al, 2004), frequency domain features, and multi-fractal analysis (Ivanov et al, 2009). In order to best classify the various types of ECG signals, algorithms in biomedical signal processing require appropriate classifiers.…”