Short- and Long-Term Joint Symbolic Dynamics of Heart Rate and Blood Pressure in Dilated Cardiomyopathy

Baumert, Mathias; Baier, V.; Truebner, Sandra; Schirdewan, Alexander; Voss, Andreas

doi:10.1109/tbme.2005.857636

Cited by 54 publications

(42 citation statements)

References 22 publications

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“…The concept of symbolic dynamics allows for an easy interpretation of physiological data by a simplified description of the system's dynamics and offers a framework for exploring the nature of a system through a means of simplified analysis. 2,5,6,30,49 By employing a coarse-graining procedure some of the time series' detailed information is lost while the robust properties of the dynamics are preserved. 6,49 In this article, we introduce a novel approach to quantify cardiorespiratory interaction that is based on the joint symbolic dynamics of respiratory phase and heart rate.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Cardiorespiratory Interactions Based on Joint Symbolic Dynamics

et al. 2011

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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.

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Section: Introductionmentioning

confidence: 99%

Quantification of Cardiorespiratory Interactions Based on Joint Symbolic Dynamics

et al. 2011

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“…For the analyses of the physiological regulatory systems (cardiovascular, cardiorespiratory) as well as the quantification of their interactions, a variety of linear as well as nonlinear methods ranging from parametric models in the time-or in the frequency domain to model-free approaches in the information domain have been proposed [71]. Linear methods are partly insufficient to quantify nonlinear structures and the complexity of physiological systems (time series)-the joint symbolic dynamics (JSD) [4,72] and high resolution joint symbolic dynamics (HRJSD) [73] overcomes the limitations.…”

Section: Symbolic Dynamicsmentioning

confidence: 99%

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

Signorini

Magenes

Ferrario

et al. 2017

Complexity and Nonlinearity in Cardiovascular Signals

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Methods from nonlinear dynamics have shown new insights into alterations of the cardiovascular system under various physiological and pathological conditions, and thus providing additional prognostic information. In this chapter prominent complexity methods of non-linear dynamics as symbolic dynamics, Poincaré plot analyses, and compression entropy are introduced and their algorithmic implementations and application examples in clinical trials are provided. Especially, we will give their basic theoretical background, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory time series analyses. IntroductionLinear time and frequency domain measures are often not sufficient enough to quantify the complex dynamics of physiological systems and their related time series. Therefore, various efforts have been made to apply nonlinear complexity measures to analyze, e.g. the heart rate variability (HRV) [1]. These approaches differ from the traditional time-and frequency domain HRV analyses because they quantify the signal properties instead of assessing only the magnitude or the frequency power of the heart rate time series. They assess the self-affinity of heartbeat fluctuations over multiple time scales (fractal measures); the regularity/irregularity or randomness of heartbeat fluctuations (entropy measures); the coarse-grained dynamics of HR fluctuations based on symbols (symbolic dynamics) and the heartbeat dynamics based on a simplified phase-space embedding [1].Symbolic dynamics is based on a coarse graining of the dynamics of a signal. The time series (in our cases the ECG or the noninvasively recorded blood pressure curve) are transformed into symbol sequences with symbols of a given alphabet. Some detail information is lost but the coarse dynamic behavior retains and

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“…(1) (2) Here, Th represents a defined threshold value which allows distinguishing high beat-to-beat variability from small changes that might be within the noise floor. For this study Th=2 was used.…”

Section: Joint Symbolic Dynamicsmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Kabir¹,

Sedaghat²,

Thomas³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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In this paper we employed joint symbolic dynamics (JSD) approach to study reproducibility of heart rate variability characteristics measured on 2 randomly selected 10-second segments within 3-minute resting orthogonal ECG in 170 healthy participants. First, the ECG R-peaks were detected using parabolic fitting. Second, the respiratory signal was derived from orthogonal ECG X-lead using QRS slopes. Third, time series of R-R intervals and respiratory phases (calculated using Hilbert transform), were transformed into tertiary symbol vectors based on their successive changes and words of length '3' were formed. Bland-Altman analysis was used to assess the agreement between measured log-transformed JSD characteristics of HRV, and their reproducibility. Traditional HRV measures such as RR' interval changes showed a very high reproducibility. However, agreement between two 10-second JSD indices of HRV was low. Interestingly, a significant decrease in low-high alterations of HRV dynamics measured using JSD was observed when respiratory phase transition intervals were excluded (10s: 4.7±9.4 vs. 24.8±21.0%, p<0.0001; 3min: 9.8±8.1 vs. 24.8±12.3%, p<0.0001).

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Short- and Long-Term Joint Symbolic Dynamics of Heart Rate and Blood Pressure in Dilated Cardiomyopathy

Cited by 54 publications

References 22 publications

Quantification of Cardiorespiratory Interactions Based on Joint Symbolic Dynamics

Quantification of Cardiorespiratory Interactions Based on Joint Symbolic Dynamics

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

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

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