Abstract：The heart rate variability (HRV) signal reflects the adjustment of autonomic nervous system on cardiac rhythm, and its dynamic characteristics can reflect the physiological function of the heart and health status. The short time HRV signal has an important value on clinical application on detecting changes in physiological and pathological conditions. We used mwords forms and forbidden words extracted from basescale entropy as the characteristic index. We compared the differences of short time HRV parameters between day and night of the subjects of healthy young people and the healthy elderly people, as well as the healthy elderly people and congestive heart failure patients. Firstly, in order to prove the effectiveness of the method, mwords forms, forbidden words were respectively applied to the sine signal, white noise and 1/f noise. We chose respectively 25 healthy young subjects, 25 healthy elderly subjects from Normal Sinus Rhythm RR Interval Database and MITBIH Normal Sinus Rhythm Database in the PhysioBank, and 20 congestive heart failure subjects from the Congestive Heart Failure RR Interval Database in the PhysioBank. The results showed that the change of mwords forms probability distribution reflected the corresponding change of autonomic regulation. In the healthy young people and the healthy elderly people, there are significant differences（P<0.05）between day and night by the forbidden words method. Moreover, the forbidden words of corresponding time periods also have significant differences（P<0.05）among two groups of subjects. In addition, compared with healthy elderly people, the forbidden words of CHF patients have been significantly reduced（219.2±6.9 vs 147.5±12.1，P<0.05）. Compared with the traditional nonlinear method, this algorithm only need a 500 heart beats series, and it provides a simple and effective method for the research and application of HRV.
刘大钊 李瑜 李锦*. 以基于基本尺度熵的心率变异性指标表征生理和病理原因引起的自主神经活动变化[J]. 中国生物医学工程学报, 2014, 33(4): 402-409.
LIU Da Zhao LI Yu LI Jin*. Base Scale Entropy Based Heart Rate Variability Indexes for Characterizing Changes in Autonomic Nerve Activity by Physiological and Pathological Modulation. journal1, 2014, 33(4): 402-409.
［1］Malik M,Camm AJ. Heart rate variability ［J］. Clinic Cordial,1990,13:570-576.
［3］Jermendy G.Clinical consequences of cardiovascular autonomic neuropathy in diabetic patients ［J］. Acta Diabetol,2003,40(2):s370-s374.
［4］Vanoli E, Adamson PB, Pinna GD, et al. Heart rate variability during specific sleep stages a comparison of healthy subjects with patients after myocardial infarction ［J］. Circulation, 1995, 91(7): 1918-1922.
［6］Yeh RG, Shieh JS, Chen GY, et al. Detrended fluctuation analysis of shortterm heart rate variability in late pregnant women ［J］. Autonomic Neuroscience, 2009, 150(1): 122-126.
［7］卞春华,马千里,司峻峰,等.短时心率变异符号序列的熵分析法 ［J］. 科学通报,2009,54(3):340-344.
［8］李锦,宁新宝.短时心率变异性信号的基本尺度熵分析 ［J］. 科学通报,2005,50(14):1438-1441.
［10］Wessel N, Ziehmann C, Kurths J, et al. Shortterm forecasting of lifethreatening cardiac arrhythmias based on symbolic dynamics ［J］. Phys Rev E, 2000, 61(1):733-739.
［11］Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability:standards and clinical user ［J］.Circulation,1996,93(5):1043-1065.
［12］Physionet Database［DB/OL］: http://www.physionet.org/cgibin/atm/ATM.2012-01-21/2012-12-16.
［14］Berntson GG, Bigger JT, Eckberg DL, et al. Heart rate variability: Origins, methods, and interpretive caveats ［J］. Psychophysiology,1997,34(6):623-648.
［17］霍铖宇,庄建军,黄晓林,等.基于Poincaré差值散点图的心率变异性分析方法研究 ［J］.物理学报,2012,61(19): 19056.