Quantification of Cardiorespiratory Interactions Based on Joint Symbolic Dynamics

Abstract

Abstract-Cardiac and respiratory rhythms are highly nonlinear and nonstationary. As a result traditional time-domain techniques are often inadequate to characterize their complex dynamics. In this article, we introduce a novel technique to investigate the interactions between R-R intervals and respiratory phases based on their joint symbolic dynamics. To evaluate the technique, electrocardiograms (ECG) and respiratory signals were recorded in 13 healthy subjects in different body postures during spontaneous and controlled breathing. Herein, the R-R time series were extracted from ECG and respiratory phases were obtained from abdomen impedance belts using the Hilbert transform. Both time series were transformed into ternary symbol vectors based on the changes between two successive R-R intervals or respiratory phases. Subsequently, words of different symbol lengths were formed and the correspondence between the two series of words was determined to quantify the interaction between cardiac and respiratory cycles. To validate our results, respiratory sinus arrhythmia (RSA) was further studied using the phase-averaged characterization of the RSA pattern. The percentage of similarity of the sequence of symbols, between the respective words of the two series determined by joint symbolic dynamics, was significantly reduced in the upright position compared to the supine position (26.4 ± 4.7 vs. 20.5 ± 5.4%, p < 0.01). Similarly, RSA was also reduced during upright posture, but the difference was less significant (0.11 ± 0.02 vs. 0.08 ± 0.01 s, p < 0.05). In conclusion, joint symbolic dynamics provides a new efficient technique for the analysis of cardiorespiratory interaction that is highly sensitive to the effects of orthostatic challenge.

Strategies of symbolization in cardiovascular time series to test individual gestational development in the fetus

“…'forbidden words') of symbolic patterns complemented information retrieved from spectral analysis in patients threatened by sudden cardiac death [7] and enhanced obstructive apnea screening [8]. This kind of transformation has also been extended to quantify time-delayed couplings between heart rate and blood pressure modulation [9] or to reflect cardiorespiratory interaction [10].…”

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

“…In 2011, Kabir et al [74] proposed a tertiary symbolization scheme and quantified the relative frequency of word types, capturing RSA patterns and thereby adding physiological a-priori knowledge to the analysis. To address the issue of different frequencies between cardiac and respiratory oscillators, Hilbert transformation was introduced to obtain the instantaneous respiratory phase (RP) sampled at the R peak in ECG, yielding beat-to-beat symbol sequences of changes in RR interval and respiratory phase.…”

Reproducibility of Heart Rate Variability Characteristics Measured on Random 10:second ECG using Joint Symbolic Dynamics

“…They are based on the differences between successive R-R intervals (an increase in RR' was represented by "+1", decrease by "−1" and no change by "0") and Rinstant respiratory phases (inspiratory phase was represented by "+1", expiratory phase by "−1" and transition from inspiratory to expiratory phase and vice versa by "0"), as described previously [3].…”

“…Compared to conventional signal-processing approaches which are inadequate for characterizing complex system dynamics, joint symbolic dynamics (JSD) has been shown to be an effective technique to provide enhanced information by employing a coarse-graining procedure to preserve the robust properties of the system's complex dynamics [2,3]. Symbolic dynamics has been widely used to study HRV dynamics and is suggested to provide improved performance for the analysis of respiratory data and cardiorespiratory interaction [3,4].…”