Implementation of time-frequency distribution software and its use to study biological signals

The joint time-frequency signal representation has received considerable attention as a powerful tool for analyzing biological signals. It combine time-domain and frequency-domain analyses to yield a potentially more revealing picture of the temporal localization of a signal\u27s spectral components. In this research we have developed algorithm which implement time-frequency signal analysis techniques on a computer system. Its primary function is to produce a variety of time-frequency representations and plots from the time series. Numerous generated signals were used to justify our computer algorithm. Variety of time-frequency distribution were utilized to expand the concept of spectral analysis of heart rate variability, to describe changes in vagal tone and sympatho-vagal balance as a function of time. As a result the assessment of the autonomic nervous system during rapid changes in heart rate was made. The smoothed Pseudo Wigner distribution was applied to electromyographic(EMG) signal during muscle fatigue. The mesh plot of the time-frequency analysis showed, the median frequency of the EMG decline during muscle fatigue

Space/time/frequency methods in adaptive radar

Radar systems may be processed with various space, time and frequency techniques. Advanced radar systems are required to detect targets in the presence of jamming and clutter. This work studies the application of two types of radar systems. It is well known that targets moving along-track within a Synthetic Aperture Radar field of view are imaged as defocused objects. The SAR stripmap mode is tuned to stationary ground targets and the mismatch between the SAR processing parameters and the target motion parameters causes the energy to spill over to adjacent image pixels, thus hindering target feature extraction and reducing the probability of detection. The problem can be remedied by generating the image using a filter matched to the actual target motion parameters, effectively focusing the SAR image on the target. For a fixed rate of motion the target velocity can be estimated from the slope of the Doppler frequency characteristic. The problem is similar to the classical problem of estimating the instantaneous frequency of a linear FM signal (chirp). The Wigner-Ville distribution, the Gabor expansion, the Short-Time Fourier transform and the Continuous Wavelet Transform are compared with respect to their performance in noisy SAR data to estimate the instantaneous Doppler frequency of range compressed SAR data. It is shown that these methods exhibit sharp signal-to-noise threshold effects. The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank methods outperform full-rank space-time adaptive processing when the space-time covariance matrix is estimated from a dataset with limited support. The utility of reduced-rank methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that reduced-rank processing has two effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio. A method for evaluating the theoretical conditioned SNR for fixed reduced-rank transforms is also presented

Time-frequency investigation of heart rate variability and cardiovascular system modeling of normal and chronic obstructive pulmonary disease (COPD) subjects

A study has been designed to add insight to some questions that have not been fully investigated in the heart rate variability field and the cardiovascular regulation system in normal and Chronic Obstructive Pulmonary Disease (COPD) subjects. It explores the correlations between heart rate variability and cardiovascular regulation, which interact through complex multiple feedback and control loops. This work examines the coupling between heart rate (HR), respiration (RESP), and blood pressure (BP) via closed-loop system identification techniques in order to noninvasively assess the underlying physiology. In the first part of the study, the applications of five different bilinear time-frequency representations are evaluated on modeled HRV test signals, actual electrocardiograms (ECG), BP and RESP signals. Each distribution: the short time Fourier transform (STFT), the smoothed pseudo Wigner-Ville (SPWVD), the ChoiWilliams (CWD), the Bom-Jordan-Cohen (BJC) and wavelet distribution (WL), has unique characteristics which is shown to affect the amount of smoothing and the generation of cross-terms. The CWD and the WL are chosen for further application because of overcoming the drawbacks of other distributions by providing higher resolution in time and frequency while suppressing interferences between the signal components. In the second part of the study, the Morlet, Meyer, Daubechies 4, Mexican Hat and Haar wavelets are used to investigate the heart rate and blood pressure variability from both COPD and normal subjects. The results of wavelet analysis give much more useful information than the Cohen\u27s class representations. Here we are able to quantitatively assess the parasympathetic (HF) and sympatho-vagal balance (LF:HF) changes as a function of time. As a result, COPD subjects breathe faster, have higher blood pressure variability and lower HRV. In the third part of the study, a special class of the exogenous autoregressive (ARX) model is developed as an analytical tool for uncovering the hidden autonomic control processes. Non-parametric relationships between the input and outputs of the ARX model resulting in transfer function estimations of the noise filters and the input filter were used as mechanistic cardiovascular models that have shown to have predictive capabilities for the underlying autonomic nervous system activity of COPD patients. Transfer functions of COPD cardiovascular models have similar DC gains but show a larger lag in phase as compared to the models of normal subjects. Finally, a method of severity classification is presented. This method combines the techniques of principal component analysis (PCA) and cluster analysis (CA) and has been shown to separate the COPD from the normal population with 100% accuracy. It can also classify the COPD population into at risk , mild , moderate and severe stages with 100%, 90%, 88% and 100% accuracy respectively. As a result, cluster and principal component analysis can be used to separate COPD and normal subjects and can be used successfully in COPD severity classification

Bilinear time-frequency representations of heart rate variability and respiration during stress

Recently, joint time-frequency signal representation has received considerable attention as a powerful tool for analyzing a variety of signals and systems. In particular, if the frequency content is time varying as in signals of biological origin which often do not comply with the stationarity assumptions, then this approach is quite attractive. In this dissertation, we explore the possibility of better representation of two particular biological signals, namely heart rate variability (HRV) and respiration. We propose the use of time-frequency analysis as a new and innovative approach to examine the physical and mental exertion attributed to exercise. Two studies are used for the main investigation, the preliminary and anticipation protocols. In the first phase of this work, the application of five different bilinear representations on modeled HRV test signals and experimental HRV and respiration signals of the preliminary protocol is evaluated. Each distribution: the short time Fourier transform (STFT), the pseudo Wigner-Ville (WVD), the smoothed pseudo Wigner-Ville (SPWVD), The Choi-Williams (CWD), and the Born-Jordan-Cohen (RID) has unique characteristics which is shown to affect the amount of smoothing and the generation of cross-terms differently . The CWD and the SPWVD are chosen for further application because of overcoming the drawbacks of the other distributions by providing higher resolution in time arid frequency while suppressing interferences between the signal components. In the second phase of this research, the SPWVD and CWD are used to investigate the presence of an anticipatory component due to the stressful exercise condition as reflected in the HRV signal from a change in behavior in the autonomic nervous system. By expanding the concept of spectral analysis of heart rate variability (HRV) into time-frequency analysis, we are able to quantitatively assess the parasympathetic (HF) and sympatho-vagal balance (LF:HF) changes as a function of time. As a result, the assessment of the autonomic nervous system during rapid changes is made. A new methodology is also proposed that adaptively uncovers the region of parasympathetic activity. It is well known that parasympathetic activity is highly correlated with the respiration frequency. This technique traces the respiration frequency and extracts the corresponding parasympathetic activity from the heart rate variability signal by adaptive filtering

Phase-space representation of digital holographic and light field imaging with application to two-phase flows

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 125-133).In this thesis, two computational imaging techniques used for underwater research, in particular, two-phase flows measurements, are presented. The techniques under study, digital holographic imaging and light field imaging, are targeted at different flow conditions. In low-density flows, particles and air bubbles in water can be imaged by a digital holographic imaging system to provide 3D flow information. In the high density case, both occlusions and scattering become significant, imaging through these partial occlusions to achieve object detection is possible by integrating views from multiple perspectives, which is the principle of light field imaging. The analyses on the digital holographic and light field imaging systems are carried out under the framework of phase-space optics. In the holographic imaging system, it is seen that, by tracking the Space bandwidth transfer, the information transformation through a digital holographic imaging system can be traced. The inverse source problem of holography can be solved in certain cases by posing proper priori constraints. As is in the application to two-phase flows, 3D positions of bubbles can be computed by well tuned focus metrics. Size statistical distribution of the bubbles can also be obtained from the reconstructed images.(cont.) Light field is related to the Wigner distribution through the generalized radiance function. One practical way to sample the Wigner distribution is to take intensity measurements behind an aperture which is moving laterally in the field. Two types of imaging systems, the light field imaging and the integral imaging, realize this Wigner sampling scheme. In the light field imaging, the aperture function is a rect function; while a sinc aperture function in the integral imaging. Axial ranging through the object space can be realized by digital refocusing. In addition, imaging through partial occlusion is possible by integrating properly selected Wigner samples.by Lei Tian.S.M

Diagnostic des machines dans le plan temps-fréquence

Using short-time Fourier transform in machinery fault diagnosis -- Time-frequency distributions and their application to machinery fault detection -- Application of wavelet transform in machine fault detection -- Time-frequency algorithms and their applications

Fixed-frequency slice computation of discrete Cohen's bilinear class of time-frequency representations

This communication derives DFT-sample-based discrete formulas directly in the spectral-correlation domain for computing fixed-frequency slices of discrete Cohen's class members with reduced computational cost, both for one-dimensional and multidimensional (specifically two-dimensional (2-D)) finite-extent sequence cases. Frequency domain integral expressions that define discrete representations are discretized to obtain these discrete implementation formulas. 2-D ambiguity function domain kernels are chosen to have separable forms for analytical convenience. Simulations demonstrating the DFT-sample-based computation in particle-location analysis of in-line Fresnel holograms are presented

Signal separation of musical instruments: simulation-based methods for musical signal decomposition and transcription

This thesis presents techniques for the modelling of musical signals, with particular regard to monophonic and polyphonic pitch estimation. Musical signals are modelled as a set of notes, each comprising of a set of harmonically-related sinusoids. An hierarchical model is presented that is very general and applicable to any signal that can be decomposed as the sum of basis functions. Parameter estimation is posed within a Bayesian framework, allowing for the incorporation of prior information about model parameters. The resulting posterior distribution is of variable dimension and so reversible jump MCMC simulation techniques are employed for the parameter estimation task. The extension of the model to time-varying signals with high posterior correlations between model parameters is described. The parameters and hyperparameters of several frames of data are estimated jointly to achieve a more robust detection. A general model for the description of time-varying homogeneous and heterogeneous multiple component signals is developed, and then applied to the analysis of musical signals. The importance of high level musical and perceptual psychological knowledge in the formulation of the model is highlighted, and attention is drawn to the limitation of pure signal processing techniques for dealing with musical signals. Gestalt psychological grouping principles motivate the hierarchical signal model, and component identifiability is considered in terms of perceptual streaming where each component establishes its own context. A major emphasis of this thesis is the practical application of MCMC techniques, which are generally deemed to be too slow for many applications. Through the design of efficient transition kernels highly optimised for harmonic models, and by careful choice of assumptions and approximations, implementations approaching the order of realtime are viable.Engineering and Physical Sciences Research Counci