Time-frequency Coherence Functions Research Articles

A Time-Varying Nonparametric Methodology for Assessing Changes in QT Variability Unrelated to Heart Rate Variability.

To propose and test a novel methodology to measure changes in QT interval variability (QTV) unrelated to RR interval variability (RRV) in nonstationary conditions. Time-frequency coherent and residual spectra representing QTV related (QTVrRRV) and unrelated (QTVuRRV) to RRV, respectively, are estimated using time-frequency Cohen's class distributions. The proposed approach decomposes the nonstationary output spectrum of any two-input one-output model with uncorrelated inputs into two spectra representing the information related and unrelated to one of the two inputs, respectively. An algorithm to correct for the bias of the time-frequency coherence function between QTV and RRV is proposed to provide accurate estimates of both QTVuRRV and QTVrRRV. Two simulation studies were conducted to assess the methodology in challenging nonstationary conditions and data recorded during head-up tilt in 16 healthy volunteers were analyzed. In the simulation studies, QTVuRRV changes were tracked with only a minor delay due to the filtering necessary to estimate the nonstationary spectra. The correlation coefficient between theoretical and estimated patterns was even for extremely noisy recordings (signal to noise ratio (SNR) in QTV dB). During head-up tilt, QTVrRRV explained the largest proportion of QTV, whereas QTVuRRV showed higher relative increase than QTV or QTVrRRV in all spectral bands ( for most pairwise comparisons). The proposed approach accurately tracks changes in QTVuRRV. Head-up tilt induced a slightly greater increase in QTVuRRV than in QTVrRRV. The proposed index QTVuRRV may represent an indirect measure of intrinsic ventricular repolarization variability, a marker of cardiac instability associated with sympathetic ventricular modulation and sudden cardiac death.

A multivariate time-frequency approach for tracking QT variability changes unrelated to heart rate variability.

The beat-to-beat variability of the QT interval (QTV) is a marker of ventricular repolarization (VR) dynamics and it has been suggested as an index of sympathetic ventricular outflow and cardiac instability. However, QTV is also affected by RR (or heart rate) variability (RRV), and QTV due to RRV may reduce QTV specificity as a VR marker. Therefore, it would be desirable to separate QTV due to VR dynamics from QTV due to RRV. To do that, previous work has mainly focused on heart rate corrections or time-invariant autoregressive models. This paper describes a novel framework that extends classical multiple inputs/single output theory to the time-frequency (TF) domain to quantify QTV and RRV interactions. Quadratic TF distributions and TF coherence function are utilized to separate QTV into two partial (conditioned) spectra representing QTV related and unrelated to RRV, and to provide an estimates of intrinsic VR dynamics. In a simulation study, a time-varying ARMA model was used to generate signals representing realistic RRV and VR dynamics with controlled instantaneous frequencies and powers. The results demonstrated that the proposed methodology is able to accurately track changes in VR dynamics, with a correlation between theoretical and estimated patterns higher than 0.88. Data from healthy volunteers undergoing a tilt table test were analyzed and representative examples are discussed. Results show that the QTV unrelated to RRV dynamics quickly increased during orthostatic challenge.

Altered Short-Term Dynamics of Cardio-Respiratory Interaction during Propofol-Induced Yawning

"Cardiac and respiratory oscillations have been shown to interact with each other. This interaction could reflect autonomic nervous system functionality. Propofol-induced yawning during anesthesia induction seems to be associated with sympathetic activation. Presumptively, there is high linearity among interaction of different physiologic system behaviors. Recently, investigators used coherence analysis to quantify the existence and strength of linearity between system signals for study of cardio-respiratory interaction under different physiological conditions. In this investigation, we used a method of time-frequency coherence function to analyze ECG and respiration signals to investigate the linearity of cardio-respiratory dynamics in patients undergoing routine propofol induction procedures for elective surgery. In this prospective, observational clinical study, a total of 84 eligible patients were enrolled. The patients were categorized into yawning and no-yawning groups during propofol induction. During induction, both groups demonstrated significant reduction in high frequency coherence (coh-HF) with simultaneously significant increase in very low frequency coherence (coh-VLF) compared to the pre-induction period. As yawning occurred, the yawning group had more significant changes of cardio-respiratory coherences than the no-yawning group at coh-LF and coh-VLF bands. The yawning group also showed loss of linearity at high frequency band (coh-HF > 0.5) as compared with the pre-induction period, and also showed increases in linearity at low (coh-LF > 0.5) and very low (coh-VLF > 0.5) frequency bands compared with the no-yawning group. Propofol-induced yawning alters cardio-respiratory dynamics with changes of linearity between cardio-vascular and respiratory system behaviors."

Damage localization using transient non-linear elastic wave spectroscopy on composite structures

To reduce the costs related to maintenance of aircraft structures, there is the need to develop new robust, accurate and reliable damage detection methods. A possible answer to this problem is offered by newly developed non-linear acoustic/ultrasonic techniques, which monitor the non-linear elastic wave propagation behaviour introduced by damage, to detect its presence and location. In this paper, a new transient non-linear elastic wave spectroscopy (TNEWS) is presented for the detection and localization of a scattered zone (damage) in a composite plate. The TNEWS analyses the uncorrelations between two structural dynamic responses generated by two different pulse excitation amplitudes by using a time–frequency coherence function. A numerical validation of the proposed method is presented. Damage was introduced and modelled using a multi-scale material constitutive model (Preisach–Mayergoyz space). The developed technique identified in a clear manner the faulted zone, showing its robustness to locate and characterize non-linear sources in composite materials

Generalized Spectral Coherences for Complex-Valued Harmonizable Processes

Complex-valued nonstationary random processes have nonvanishing complementary second-order moment functions. In this paper, we propose generalized dual-frequency and time-frequency coherence functions for harmonizable processes. The proposed generalized spectral coherences are based on widely linear estimators, and they result in coherence measures that combine Hermitian and complementary moment functions. We show that for analytic processes, and more surprisingly also for real-valued processes, additional second-order information becomes available through the generalized coherences. We offer illuminating geometrical interpretations of the proposed coherences through Hilbert space inner product formulations. Finally, we extend the theory to generalized cross-coherences between pairs of harmonizable processes

Development of a time-frequency representation for acoustic detection of buried objects

A recently developed apparatus permits the detection of buried objects, such as landmines, by remotely sensing the variations of ground vibration that occur over the buried object with a laser Doppler vibrometer (LDV), when the ground is insonified by means of acoustic-to-seismic coupling. As it is currently implemented, the LDV scans individual points on the ground. The technique shows much promise, but it is slow when compared to some other techniques. This work demonstrates that mines can be detected as the LDV beam moves continuously across the ground, by using an optimal time-frequency representation of the LDV signals. This improvement has the potential to significantly increase scanning speeds. The vibrometer output signal is analyzed by means of time-frequency representations, which exhibit characteristic acoustic “signatures” when scanning over buried objects. The most efficient representation appears to be the smoothed spectrogram weighted by the time-frequency coherence function. It detects the searched signal energy enhancements while filtering out most features due to speckle noise. Detection is then efficiently achieved by searching simultaneous extrema of the marginals and moments of this representation. Experiments show that scanning speeds up to 3.6 km/h can be achieved for successful detection of buried landmines in outdoor ground.

Approximate Time-Variable Coherence Analysis of Multichannel Signals

We present a new method for signal extraction from noisy multichannel epileptic seizure onset EEG signals. These signals are non-stationary which makes time-invariant filtering unsuitable. The new method assumes a signal model and performs denoising by filtering the signal of each channel using a time-variable filter which is an estimate of the Wiener filter. The approximate Wiener filters are obtained using the time-frequency coherence functions between all channel pairs, and a fix-point algorithm. We estimate the coherence functions using the multiple window method, after which the fix-point algorithm is applied. Simulations indicate that this method improves upon its restriction to assumed stationary signals for realistically non-stationary data, in terms of mean square error, and we show that it can also be used for time-frequency representation of noisy multichannel signals. The method was applied to two epileptic seizure onset signals, and it turned out that the most informative output of the method are the filters themselves studied in the time-frequency domain. They seem to reveal hidden features of the epileptic signal which are otherwise invisible. This algorithm can be used as preprocessing for seizure onset EEG signals prior to time-frequency representation and manual or algorithmic pattern classification.