Cardiovascular and Cardiorespiratory Signals Complexity Analysis Using Different Techniques

In recent decades, the concept of complex physiological systems has become more and more popular. The evaluation of the biological time series' dynamic complexity is an essential subject with possible applications such as the characterization of physiological states i.e. HRV, BP, and RESP signals and pathological disorders to the measurement of diagnostic parameters. The convergence of several physiological regulation processes is the cause of heterogeneity in cardiovascular time series, that consider many factors and function over several time scales, resulting not only the presence of short-term dynamics but also the coexistence of long-range correlations in various physiological signals. The most popular approach to evaluating the dynamic complexity and irregularity of time series over multiple time scales is entropy based analysis. The most used approach is multiscale entropy (MSE) and refined MSE (RMSE). It is then added to the heart period time series, respiration time series, and blood pressure time series, measured in young subjects and old subjects under resting conditions. This research applies to short-term cardiovascular and cardiorespiratory variability documents that LMSE can better describe physiological processes' behavior causing biological oscillations at various time scales than RMSE.