Retrieval of self-coupled harmonics

Zhou, G.T.; Giannakis, Georgios B.

doi:10.1109/78.382402

Cited by 34 publications

(1 citation statement)

References 24 publications

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“…Nonlinear transformation causes the appearance of self-coupling peaks in the bispectrum (Nikias andPetropulu 1993, Zhou andGiannakis 1995). In periodic signals, peaks at the selffrequency without self-phase couplings are common.…”

Section: Data Acquisition and Analysis Techniquesmentioning

confidence: 99%

Nonlinear cardio-respiratory interactions revealed by time-phase bispectral analysis

2004

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Abstract. Bispectral analysis based on high order statistics, introduced recently as a technique for revealing time-phase relationships among interacting noisy oscillators, has been used to study the nature of the coupling between cardiac and respiratory activity. Univariate blood flow signals recorded simultaneously by laser-Doppler flowmetry on both legs and arms were analysed. Coupling between cardiac and respiratory activity was also checked by use of bivariate data and computation of the cross-bispectrum between the ECG and respiratory signals. Measurements were made on six healthy males aged 25-27 years. Recordings were taken during spontaneous breathing (20 min), and during paced respiration at frequencies both lower and higher than that of spontaneous respiration (either two or three recordings with a constant frequency in the interval between 0.09 and 0.35 Hz). At each paced frequency recordings were taken for 12 min. It was confirmed that the dynamics of blood flow can usefully be considered in terms of coupled oscillators, and demonstrated that interactions between the cardiac and respiratory processes are weak and time-varying, and that they can be nonlinear. Nonlinear coupling was revealed to exist during both spontaneous and paced respiration. When present, it was detected in all four blood flow signals and in the cross-bispectrum between the ECG and respiratory signal. The episodes with nonlinear coupling were detected in 11 out of 22 recordings and lasted between 19 s in the case of high frequency (0.34 Hz) and 106 s in the case of low frequency paced respiration (0.11 Hz).

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Section: Data Acquisition and Analysis Techniquesmentioning

confidence: 99%

Nonlinear cardio-respiratory interactions revealed by time-phase bispectral analysis

2004

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Statistical Signal Processing, Higher Order Tools

Giannakis

Zhou

1999

Wiley Encyclopedia of Electrical and Electronics Engineering

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The sections in this article are Motivation and Overview Basic Background Discerning Non‐Gaussianity Recovering Phase Information Checking and Identifying Nonlinearities Separating Multiple Sources Conclusion and Future Directions Acknowledgment

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Detection of Nonlinearity in Cardiovascular Variability Signals using Cyclostationary Analysis

Seydnejad

2007

Ann Biomed Eng

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A novel approach for detection of polynomial nonlinearity in the neuro-cardiovascular system based on cyclostationary analysis is presented. Metronome breathing is employed to provide a sinusoidal input to the neuro-cardiovascular system in which Heart Rate Variability (HRV) and Blood Pressure Variation (BPV) are considered as its outputs. The presence of new harmonics of the main respiratory rate in the HRV and BPV is investigated by using the concept of (self) phase and (self) frequency coupling. It is shown that a second order polynomial nonlinear system is actually involved in producing the HRV and BPV. The strength of this nonlinearity decreases with increasing the breathing rate.

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Retrieval of self-coupled harmonics

Cited by 34 publications

References 24 publications

Nonlinear cardio-respiratory interactions revealed by time-phase bispectral analysis

Nonlinear cardio-respiratory interactions revealed by time-phase bispectral analysis

Statistical Signal Processing, Higher Order Tools

Detection of Nonlinearity in Cardiovascular Variability Signals using Cyclostationary Analysis

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