应用基本尺度熵分析心率变异性

发布时间：2018-08-21 12:49

【摘要】：心脏是保证人体生命活动正常进行的核心器官,它受到神经、血压、激素等多种因素的调节。近年来,心脏疾病一直是威胁人类健康的主要疾病之一,对心脏功能进行准确的评估和诊断,成为一个重要的研究课题。 心电信号蕴含着心脏电活动丰富的生理信息,在临床医学与研究中易于采集和检测,具有较直观的规律性,是当前临床医学与生物科学领域研究最多的心脏电信号。从心电信号中提取的心率变异性(Heart rate variability, HRV)信号在近二十年的研究中受到了普遍的重视。作为反映自主神经系统活动水平的灵敏指标,对HRV的提取和分析已经在定量评估交感神经和副交感神经活动的紧张性、均衡性及其对心血管系统的影响方面得到了广泛的应用。 近年来,熵分析法广泛应用到HRV的分析中,并取得了一定的进展。该方法以其方法简单、运算快速、抗干扰强等优点为探测和捕捉时间序列中的有用信息提供了方便。由于心率变异性信号是非平稳、有噪声干扰的时间序列,所以本文采用非线性动力学分析方法中的基本尺度熵方法,在前人研究的基础上对HRV信号进行了分析,主要研究工作和创新点如下： (1)研究了改变基本尺度熵方法中的延迟时间对熵值的影响。通过Logistic映射序列、1/f噪声序列和HRV信号序列,发现延迟时间取L=1时基本尺度熵并没达到最大值,当L≥3时,熵值收敛于一个固定值,该值非常接近理论推导值log2(4m)。因此,通过实验仿真得出最佳延迟时间参数为L=3,此时基本尺度熵值能够真实全面地反映时间序列的复杂程度,为HRV分析方法中其他参数的选取提供一定的参考价值。 (2)计算了五种不同生理、病理状态人群的基本尺度熵值与“禁止状态”个数,得出两者的变化关系,并应用多尺度化的基本尺度熵分析量化HRV序列在多个时间尺度下波动的不规则度,得出其复杂性指数。结果表明健康年轻人的计算结果代表了最佳的生理健康状态,而其他生理、病理人群由于心脏病变,自主神经系统紊乱,造成心率变异性的复杂性有所下降。 (3)设计、实施了颠倒作息的实验,采集了六名测试者正常作息24h和颠倒作息24h的心电信号,经过数据预处理,从中提取HRV信号。联合基本尺度熵、MSE曲线和m-words组合形式分布直方图分析颠倒作息情况下睡眠、清醒两种状态的HRV信号,与正常作息下的变化规律进行比较。结果表明人体睡眠清醒循环比昼夜节律24小时交替循环对心脏的搏动特性影响更大,清醒和睡眠状态决定了自主神经系统的相互作用规律与HRV信号混沌特性,同时对人体本身动力学复杂性产生影响。
[Abstract]:The heart is the core organ which guarantees the normal human life activity. It is regulated by many factors such as nerve, blood pressure, hormone and so on. In recent years, heart disease has been one of the main diseases threatening human health. The accurate evaluation and diagnosis of heart function has become an important research topic. Electrocardiogram (ECG), which contains abundant physiological information of cardiac electrical activity, is easy to collect and detect in clinical medicine and research, and has more intuitive regularity. It is the most researched ECG signal in the field of clinical medicine and biological science. Heart rate variability (Heart rate variability, HRV) signals extracted from ECG signals have received widespread attention in recent 20 years. As a sensitive index to reflect the level of autonomic nervous system activity, the extraction and analysis of HRV have been widely used in the quantitative evaluation of sympathetic and parasympathetic nervous system tension, balance and its influence on cardiovascular system. In recent years, entropy analysis has been widely used in the analysis of HRV, and some progress has been made. This method provides convenience for detecting and capturing useful information in time series because of its simple method, fast operation and strong anti-interference. Because the signal of heart rate variability is non-stationary and noisy time series, in this paper, the basic scale entropy method of nonlinear dynamics analysis is used to analyze the HRV signal on the basis of previous studies. The main research works and innovations are as follows: (1) the effect of delay time on entropy is studied. By using 1 / f noise sequence and HRV signal sequence of Logistic mapping sequence, it is found that the entropy of the basic scale of delay time L = 1 does not reach the maximum. When L 鈮，

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