Short-Term Analysis Of Cardiac Dynamics Based On Entropy Measures

The early noninvasive and nondestructive detection of cardiovascular diseases is a contemporary challenge in the field of biomedicine. Concepts from cardiac dynamics appear to provide it promising methods through the assessment of the integrated behaviors of the cardiovascular signals on a system level. Entropy measures are commonly applied in cardiac dynamics. They have been shown to be very promising in the dynamical analysis of physiological series with finite length and various noises.This dissertation aimed to probe deeply into the significance of short-term cardiac dynamics in the noninvasive and nondestructive detection of cardiovascular diseases. Briefly, quantitative indices for the evaluation of statistical abilities were defined first and the stability as well as the consistency of conventional entropy measures were compared. Then this dissertation established a novel distribution entropy measure and its multiscale and multivariate counterparts. Their statistical abilities were also examined rigorously. Finally, the significance of short-term cardiac dynamical features estimated by distribution entropy was explored with real-world cardiovascular data. Main works in this dissertation are listed as follows.(1) A physical fuzzy membership function was defined by introducing an adjustable factor λ; a refined family of fuzzy entropy was developed accordingly. Simulation results indicated that the refined algorithms had clearly improved stability in comparison with the original ones. Their consistency was highly comparable with the original algorithms. The value of λ depended on the noise level of the time-series; the noise level would be over-estimated by relatively large λ, which could consequently affect the consistency. Its value could thus be selected empirically by a compromise between the stability and consistency.(2) Systematically methods for the evaluation of the statistical abilities of entropy measures were established. Prior to the evaluation, a distinguishability index and a reliability index was defined. This work then implemented elaborate tests on the consistency of both cross entropies and multivariate entropies through4distinctly different theoretical models-coupled broadband noise model, coupled MIX(p) process, coupled Henon map, and coupled Rossler system. Results indicated that cross entropies failed to work in nonlinear chaotic systems although they could work relatively well in linear stochastic processes. Multivariate entropies could, however, work well in both; they were also less dependent on input parameters in comparison with cross entropies. However, the low-frequency trend in time-series could affect the performances of both multivariate entropy measures. Their abilities could not be further improved by multiscale analysis.(3) A novel distribution entropy and its multivariate and multiscale counterparts were developed. It indicated in this dissertation that conventional entropy measures did not fully characterize the information carried in the state space, which accounted much for their inconsistency and instability. This work developed a novel distribution entropy metric by integrating the information carried therein. Besides, it established a joint procedure for multivariate state-space reconstruction. The multivariate distribution entropy was developed accordingly. Their stability and consistency were subsequently examined quantitatively and rigorously. Results showed distribution entropy a very robust metric for assessing the complexity. In comparison with both sample entropy and refined fuzzy entropy, it had significantly improved consistency and stability. It was also very stable in data sets in as small as50data points. Besides, multivariate distribution entropy inherited the advantages of distribution entropy, indicating by far better performances in assessing the multivariate complexity of simulation models compared to conventional algorithms.(4) Aging effects on the short-term cardiac electrodynamical features were investigated in this dissertation, which should probably be a new dynamical picture of the autonomic control. Results indicated that both the distribution entropy and the refined fuzzy entropy had significant differences among different aging groups. However, conventional measures failed to captures these differences. In comparison with refined fuzzy entropy, distribution entropy showed more significant varying patterns with aging. The distribution entropy of short-term heartbeat interval data at the first scale declined significantly in group with age>40year. But for old people whose ages were70year and above, the distribution entropy did not reduce further; it maintained in a relatively low level. By comparison, no significant variation was captured in distribution entropy at higher scales.(5) This dissertation had explored, for the first time, the aging effects on the short-term cardiac mechano-dynamical features and the electromechanical coupling, which should provide valuable tools for the noninvasive and nondestructive detection of cardiovascular diseases. Results showed that distribution entropy of short-term cardiac diastolic period was highly comparable with that of heartbeat interval data in healthy young adults. The multivariate distribution entropy of heart rate-diastolic period was relative large, showing a tight coupling between them. The distribution entropy of the cardiac diastolic period was significantly lower than that of heartbeat interval data. Their coupling was also declined and this declining tendency maintained with aging. Those results suggested a possible mechanism of aging induced damage in cardiac mechanic function which should affect the contraction and relaxation of the heart.(6) Noninvasive and nondestructive indices for the detection of heart failure were established by short-term cardiac dynamics. Clinical trials showed that the electrodynamical, mechano-dynamical features and the electro-mechanical coupling at the first scale all significantly depressed in heart failure patients. Additionally, these features were also distinctly declined at higher scales, which were different from the affects of aging. There was a certain merit of short-term cardiac electrodynamical features in HF detection. However, the short-term cardiac mechano-dynamical features and the electro-mechanical coupling could indeed be capable of providing additional valuable information in this specific task. The index constructed by all dynamical features resulted obviously improved performances with sensitivity of96.15%and specificity of100%, in comparison with that developed by solely electrodynamical features, whose sensitivity and specificity was92.31%and96.00%, respectively.