Existing methods of physiological signal analysis based on nonlinear dynamic theories only examine the complexity difference of the signals under a single sampling frequency.We developed a technique to measure the multifractal characteristic parameter intimately associated with physiological activities through a frequency scale factor.This parameter is highly sensitive to physiological and pathological status.Mice received various drugs to imitate different physiological and pathological conditions,and the distributions of mass exponent spectrum curvature with scale factors from the electrocardiogram (ECG) signals of healthy and drug injected mice were determined.Next,we determined the characteristic frequency scope in which the signal was of the highest complexity and most sensitive to impaired cardiac function,and examined the relationships between heart rate,heartbeat dynamic complexity,and sensitive frequency scope of the ECG signal.We found that all animals exhibited a scale factor range in which the absolute magnitudes of ECG mass exponent spectrum curvature achieve the maximum,and this range (or frequency scope) is not changed with calculated data points or maximal coarse-grained scale factor.Further,the heart rate of mice was not necessarily associated with the nonlinear complexity of cardiac dynamics,but closely related to the most sensitive ECG frequency scope determined by characterization of this complex dynamic features for certain heartbeat conditions.Finally,we found that the health status of the hearts of mice was directly related to the heartbeat dynamic complexity,both of which were positively correlated within the scale factor around the extremum region of the multifractal parameter.With increasing heart rate,the sensitive frequency scope increased to a relatively high location.In conclusion,these data provide important theoretical and practical data for the early diagnosis of cardiac disorders.
YANG XiaoDongHE AiJunLIU PengSUN TongFengNING XinBao
Physiological signal belongs to the kind of nonstationary and time-variant ones.Thus,the nonlinear analysis methods may be better to disclose its characteristics and mechanisms.There have been plenty of evidences that physiological signal generated by complex self-regulated system may have a fractal structure.In this work,we introduce a new measure to characterize multifractality,the mass exponent spectrum curvature,which can disclose the complexity of fractal structure from total bending degree of the spectrum.This parameter represents the nonlinear superpositions of the discrepancies of fractal dimension from all adjacent points in the curve and therefore solves the problem of original parameters for not fully reflecting the information of entire subsets in the fractal structure.The evaluations of deterministic fractal system Cantor measure validate that it is completely effective in exploring the complexity of chaotic series,and is also not affected by nonstability of the signal as well as disturbances of the noises.We then apply it to the analysis of human heart rate variability(HRV) signals and sleep electroencephalogram(EEG) signals.The experimental results show that this method can be better to discriminate cohorts under different physiological and pathological conditions.Compared with the indicator of singularity spectrum width,there are some improvements both on the computing efficiency and accuracy.Such conclusion may provide some valuable information for clinical diagnoses.
