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| Base Scale Entropy Based Heart Rate Variability Indexes for Characterizing Changes in Autonomic Nerve Activity by Physiological and Pathological Modulation |
| College of Physics and Information Technology, Shaanxi Normal University, Xi’an 710062, China |
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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.
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