Dynamical complexity detection in short-term physiological series using base-scale entropy.
Physiological systems generate complex fluctuations in their output signals that reflect the underlying dynamics. The base-scale entropy method was proposed as a complexity measure to investigate the complexity of time series. The advantages of this method are simplicity and extremely fast calculation for very short data sets. This method enables analyzing very short, nonstationary, and noisy data sets. We employed this method for short-term physiological time series for analysis of heart-rate variability signals. The results show that the simple and easily calculated measure can effectively detect the complexity dissimilarity of physiological time series in different physiological or pathological states, which is convenient for clinical applications.
Li
Jin
J
State Key Laboratory of Modern Acoustics, Institute for Biomedical Electronical Engineering, Nanjing University, Nanjing 210093, China.
Ning
Xinbao
X
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D019277:Entropy; D006801:Humans; D008954:Models, Biological; D010827:Physiology / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors