Abstract
As we age, our hearts undergo changes that result in a reduction in complexity of physiological interactions between different control mechanisms. This results in a potential risk of cardiovascular diseases which are the number one cause of death globally. Since cardiac signals are nonstationary and nonlinear in nature, complexity measures are better suited to handle such data. In this study, three complexity measures are used, namely Lempel–Ziv complexity (LZ), Sample Entropy (SampEn) and Effort-To-Compress (ETC). We determined the minimum length of RR tachogram required for characterizing complexity of healthy young and healthy old hearts. All the three measures indicated significantly lower complexity values for older subjects than younger ones. However, the minimum length of heart-beat interval data needed differs for the three measures, with LZ and ETC needing as low as 10 samples, whereas SampEn requires at least 80 samples. Our study indicates that complexity measures such as LZ and ETC are good candidates for the analysis of cardiovascular dynamics since they are able to work with very short RR tachograms.
Highlights
It is well known that functions of all physiological systems are greatly altered during the process of aging
For analysis with four bins, the mean Lempel–Ziv complexity (LZ) complexity of RR tachograms of old subjects is significantly less than that of young subjects for data lengths of 15 and higher while the same is true for data lengths of 10 and higher, while using eight bins
Complexity measures are very popular for studying cardiac data and they are increasingly being used for detection, analysis and classification in cardiac applications
Summary
It is well known that functions of all physiological systems are greatly altered during the process of aging. The cardiovascular system has received prominent attention due to the high death rate attributed to heart related diseases. Cardiac aging has been an important area for research and clinical studies, and understanding heart rate patterns is an essential step in this study. Heart rate variability (HRV), defined as the variation in the interval between consecutive heart beats, has been proposed as a promising noninvasive quantitative marker for studying autonomic activity, useful for research and clinical studies (Dreifus et al, 1993). HRV has been adopted as the conventionally accepted term to describe variations of both instantaneous heart rate and the time interval between successive heart beats. While dealing with consecutive cardiac cycles, researchers have used other terms like heart period
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