| Heart is a core organ of the body to maintain normal life activities. It is regulated by multi-factor, such as nerve, blood pressure, hormone, et al. Over the years, heart disease has been one of the major diseases threatening human health. It has become an important research topic to evaluate and diagnose accurately cardiac function.Electrocardiogram signal contains the abundant physiological information of cardiac electrical activity. It is easy to collect and detect in clinical medicine and research. Electrocardiogram signal has more intuitive regularity and become one of the most important objects of study in the clinical medicine and biological sciences. Heart rate variability signals extracted from the ECG receive widespread attention in the two decades. As a sensitive index of reflecting the activity level of autonomic nervous system, analysis of HRV has been widely used in assessing sympathetic、 parasympathetic nerve activity and the impact to the cardiovascular system.In recent years, entropy analysis method is widely applied to the HRV analysis, and has made some progress. Because the entropy analysis is simple, fast and strong anti-interference, this method provides convenience to detect and capture useful information of time series. The heart rate variability signals are usually non-stationary and noisy, so we used base-scale entropy method to analyze HRV signals on the basis of previous studies in this paper, the main research works and innovation points are as follows:(1) By changing the delay time of base-scale entropy method, we studied the influence of the entropy values. By Logistic mapping sequence,1/f noise sequence and HRV signals sequence, we found that base-scale entropy does not reach the maximum when the delay time L=1. The base scale entropy converge to a fixed value when L≥3, the value is very close to the theoretical derivation values log2(4m). Therefore, we get the best delay time parameter for L=3through the experiment simulation, which the entropy values can properly reflect complexity of time series and provide reference value for the selection of other parameters in HRV signal analysis methods.(2) For five different physiological pathological state, we calculated the base scale entropy and forbidden words, and concluded the relationship between base scale entropy and forbidden words. In addition, we quantified fluctuation irregularity of HRV series at multiple time scale and calculated their complexity index by multiscale base-scale entropy. The results show that the calculation results of healthy young people represent the best state of physiological health, and other physiological、 pathology people due to heart disease and autonomic nervous system disorder cause a drop in the complexity of the heart rate variability.(3) We designed and carried out reversed schedule experiment to collect24-hour ECG signals of six subjects respectively at normal schedule state and reversed schedule state. We extracted the HRV signals after data preprocessing. We researched the HRV signals of sleep and awake states in the reversed schedule cases, and compared with normal schedule by base-scale entropy、multiscale base-scale entropy curves and the distribution plot of m-words forms. The results show that the human sleep-wake cycle has greater impact on heart beat characteristics than24hour cycle of circadian rhythm. Awake and sleep state decide the interaction of the autonomic nervous system and chaos characteristic of HRV signals. At the same time, awake and sleep state has impact on the dynamic complexity of human body. |
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