Validating phase relations between cardiac and breathing cycles during sleep.
ABSTRACT The objective of this article is to investigate phase relations between heart beat and breathing cycles and their dependence on sleep states. Furthermore, the appearance of phase synchronizations between these signals is proved statistically by analyzing the phase relations between breathing and heart beat periods. The phase synchronizations between these signals are searched for by testing appropriate parameters of surrogate data with similar power spectra but randomly shuffled phase relations.
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ABSTRACT: Phase synchronization (PS) analysis has been demonstrated to be a useful method to infer functional connectivity with multichannel neural signals, e.g., electroencephalography (EEG). Methodological problems on quantifying functional connectivity with PS analysis have been investigated extensively, but some of them have not been fully solved yet. For example, how long a segment of EEG signal should be used in estimating PS index? Which methods are more suitable to infer the significant level of estimated PS index? To address these questions, this paper performs an intensive computation study on PS analysis based on surrogate tests with 1) artificial surrogate data generated by shuffling the rank order, the phase spectra, or the instantaneous frequency of original EEG signals, and 2) intersubject EEG pairs under the assumption that the EEG signals of different subjects are independent. Results show that 1) the phase-shuffled surrogate method is workable for significance test of estimated PS index and yields results similar to those by intersubject EEG surrogate test; 2) generally, a duration of EEG waves covering about 3 16 cycles is suitable for PS analysis; and 3) the PS index based on mean phase coherence is more suitable for PS analysis of EEG signals recorded at relatively low sampling rate.IEEE transactions on bio-medical engineering 05/2012; 59(8):2254-63. DOI:10.1109/TBME.2012.2199490 · 2.23 Impact Factor
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ABSTRACT: Extensions to a previously published nonlinear model for generating realistic artificial electrocardiograms to include blood pressure and respiratory signals are presented. The model accurately reproduces many of the important clinical qualities of these signals such as QT dispersion, realistic beat to beat variability in timing and morphology and pulse transit time. The advantage of this artificial model is that the signal is completely known (and therefore its clinical descriptors can be specified exactly) and contains no noise. Artifact and noise can therefore be added in a quantifiable and controlled manner in order to test relevant biomedical signal processing algorithms. Application examples using Independent Component Analysis to remove artifacts are presented.Proceedings of SPIE - The International Society for Optical Engineering 05/2004; DOI:10.1117/12.544525 · 0.20 Impact Factor