Poincaré Plot Methods for Heart Rate Variability Analysis by Ahsan Habib Khandoker, Chandan Karmakar, Michael Brennan,

By Ahsan Habib Khandoker, Chandan Karmakar, Michael Brennan, Marimuthu Palaniswami, Andreas Voss

The Poincaré plot (named after Henri Poincaré) is a well-liked two-dimensional visualization device for dynamic structures as a result of its intuitive show of the dynamic houses of a process from a time sequence. This publication provides the root of Poincaré plot and concentration particularly on conventional and new equipment for analysing the geometry, temporal and spatial dynamics disclosed by way of the Poincaré plot to guage center expense variability (HRV). Mathematical descriptors of Poincaré plot were built to quantify the autonomic fearful method job (sympathetic and parasympathetic modulation of middle rate). Poincaré plot research has additionally been utilized in numerous medical diagnostic settings like diabetes, power center failure, power renal failure and sleep apnea syndrome. the first goals of quantification of the Poincaré plots are to discriminate fit physiological platforms from pathological stipulations and to categorise the level of a disorder. The HRV research through Poincaré plot has spread out plentiful possibilities for very important scientific and study purposes. for that reason, the current booklet can be utilized both for self-study, as a complement to classes in linear and nonlinear structures, or as a contemporary monograph through researchers during this box of HRV analysis.

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In this case, Eq. 11 provides us with an accurate approximation to the RR interval series generated by our HRV model. This result holds so long as the intervals are approximately regular and the modulation is slow. This is generally the case for RR intervals. However, for subjects with very large HRV the assumption that the intervals are approximately regular may be somewhat inaccurate. 32π . 1 Length of Poincar´e Plot Main Cloud In this section we develop an approximation to the length of a Poincar´e plot, depicted in Fig.

The LF and HF parameters were calculated by using the AR technique with the modified covariance technique [123]. 4 Hz. First, we derive the length and width of the Poincar´e plot from the LF and HF power by using Eq. 22 and compare the derived length and width with the actual length and width by plotting them against each other as scatter plot. The closer the values are to being identical, the closer the scatter plot resembles the line of identity. 22 requires the values of the constants ωs , ω p and HR.

Subplots (c) and (d) show how the analytic approximations to length and width compare. Figure adapted from Brennan et al. 3 Sympathetic-Parasympathetic Balance In this scenario, levels of parasympathetic respiratory activity are introduced. This is simulated by way of a small coupling intensity for the parasympathetic respiratory oscillator in addition to a high level of sympathetic coupling. 05. A large degree of variability emerges in the model’s output, in which the parasympathetic oscillator is responsible for the flanging effect or widening of the cigar shape into a “comet”.

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