Time-variant partial directed coherence in analysis of the cardiovascular system. A methodological study

Milde, T.; Schwab, Karin; Walther, Mario; Eiselt, M.; Schelenz, C; Voss, Andreas; Witte, Herbert

doi:10.1088/0967-3334/32/11/s06

Cited by 28 publications

(27 citation statements)

References 36 publications

(55 reference statements)

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“…They showed that the presence of instantaneous correlations may produce misleading profiles of PDC, whereas ePDC and iPDC provided a correct interpretation of extended and lagged causality, suggesting that ePDC and iPDC are more interpretable than PDC when applied to known cardiovascular and neuronal data. Milde et al [23] introduced a time-variant version of PDC (tvPDC) for short-term multivariate data analysis which is based on a time-variant MAR model in combination with a Kalman filter for model parameter estimation. The Fourier transform of the AR parameters is used to define tvPDC.…”

Section: (I) Linear Methodsmentioning

confidence: 99%

Cardiovascular and cardiorespiratory coupling analyses: a review

Schulz

Adochiei

Edu

et al. 2013

Phil. Trans. R. Soc. A.

139

127

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Recently, methods have been developed to analyse couplings in dynamic systems. In the field of medical analysis of complex cardiovascular and cardiorespiratory systems, there is growing interest in how insights may be gained into the interaction between regulatory mechanisms in healthy and diseased persons. The couplings within and between these systems can be linear or nonlinear. However, the complex mechanisms involved in cardiovascular and cardiorespiratory regulation very likely interact with each other in a nonlinear way. Recent advances in nonlinear dynamics and information theory have allowed the multivariate study of information transfer between time series. They therefore might be able to provide additional diagnostic and prognostic information in medicine and might, in particular, be able to complement traditional linear coupling analysis techniques. In this review, we describe the approaches (Granger causality, nonlinear prediction, entropy, symbolization, phase synchronization) most commonly applied to detect direct and indirect couplings between time series, especially focusing on nonlinear approaches. We will discuss their capacity to quantify direct and indirect couplings and the direction (driver–response relationship) of the considered interaction between different biological time series. We also give their basic theoretical background, their basic requirements for application, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory coupling analyses.

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Section: (I) Linear Methodsmentioning

confidence: 99%

Cardiovascular and cardiorespiratory coupling analyses: a review

Schulz

Adochiei

Edu

et al. 2013

Phil. Trans. R. Soc. A.

139

127

View full text Add to dashboard Cite

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“…Due to, that the original introduced PDC method cannot be applied to non-stationary signals a time-variant version is needed providing information about the partial correlative short-time interaction properties. In addition to, Milde et al [31], the normalized short time partial directed coherence (NSTPDC) was introduced for nonstationary signal to evaluate dynamic coupling changes and to detect the level and direction of couplings in multivariateand complex dynamic systems [32]. With the view to determining the coupling strength and direction between two time series, x and y, a coupling factor (CF) was proposed.…”

Section: Normalized Short Time Partial Directed Coherencementioning

confidence: 99%

Analyses of Heart Rate, Respiration and Cardiorespiratory Coupling in Patients with Schizophrenia

Schulz

Bär

Voss

2015

Entropy

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Schizophrenia is a severe mental disorder associated with a significantly increased cardiovascular mortality rate. However, the underlying mechanisms leading to this cardiovascular disease (CVD) are not fully known. Therefore, the objective of this study was to characterize the cardiorespiratory influence by investigating heart rate, respiration and the causal strength and direction of cardiorespiratory coupling (CRC), based mainly on entropy measures. We investigated 23 non-medicated patients with schizophrenia (SZ), comparing them to 23 age-and gender-matched healthy controls (CO). A significantly reduced complexity was found for the heart rate and a significantly increased complexity in respiration and CRC in SZ patients when compared to corresponding measurements from CO (p < 0.001). CRC analyses revealed a clear coupling, with a driver-responder relationship from respiration to heart rate in SZ patients. Moreover, a slight driver-responder relationship from heart rate to respiration could be recognized. These findings lead to the assumption that SZ should be considered to be a high-risk group for CVD. We hypothesize that the varying cardiorespiratory regulation contributes to the increased risk for cardiac mortality. Therefore, regular monitoring of the cardiorespiratory status of SZ is suggested to identify autonomic regulation impairment at an early stage-to develop timely and effective treatment and intervention strategies. OPEN ACCESSEntropy 2015, 17 484

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“…In contrast, the instantaneous heart rate (IHR) representation is an approximation of the demodulation. The superiority of the LPFES (vs. IHR) for the investigation of rhythmic HRV components was shown by Milde et al [23]. The final HRV representation was obtained via multiplication of the LPFES with the sampling rate and with 60 beats per minute (bpm) and downsampled to 8 Hz.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure

Piper¹,

Schiecke

Leistritz

et al. 2014

Biomedical Engineering / Biomedizinische Technik

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An innovative concept for synchronization analysis between heart rate (HR) components and rhythms in EEG envelopes is represented; it applies time-variant analyses to heart rate variability (HRV) and EEG, and it was tested in children with temporal lobe epilepsy (TLE). After a removal of ocular and movement-related artifacts, EEG band activity was computed by means of the frequency-selective Hilbert transform providing envelopes of frequency bands. Synchronization between HRV and EEG envelopes was quantified by Morlet wavelet coherence. A surrogate data approach was adapted to test for statistical significance of time-variant coherences. Using this processing scheme, significant coherence values between a HRV low-frequency sub-band (0.08-0.12 Hz) and the EEG δ envelope (1.5-4 Hz) occurring both in the preictal and early postictal periods of a seizure can be shown. Investigations were performed for all electrodes at 20-s intervals and for selected electrode pairs (T3÷C3, T4÷C4) in a time-variant mode. Synchronization was more pronounced in the group of right hemispheric TLE patients than in the left hemispheric group. Such a group-specific augmentation of synchronization confirms the hypothesis of a right hemispheric lateralization of sympathetic cardiac control of the low-frequency HRV components.

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Time-variant partial directed coherence in analysis of the cardiovascular system. A methodological study

Cited by 28 publications

References 36 publications

Cardiovascular and cardiorespiratory coupling analyses: a review

Cardiovascular and cardiorespiratory coupling analyses: a review

Analyses of Heart Rate, Respiration and Cardiorespiratory Coupling in Patients with Schizophrenia

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure

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