This article considers the problem of phase synchrony and coherence analysis using a modified version of the Stransform, referred to here as the Modified S-transform (MST). This is a novel and important time-frequency approach to study the phase coupling between two or more different spatially recorded entities with nonstationary characteristics. The basic method includes a cross-spectral analysis to study the phase synchrony of nonstationary signals, and relies on some properties of the MST, such as phase preservation. We demonstrate the usefulness of the technique using simulated examples and real newborn EEG data. The results show the advantage of using the cross-MST in the study of the connectivity between different signals using the time-frequency coherence. The MST led to improvements in resolution of almost twofold over the standard S-Transform in the examples presented in the article.

Laser Doppler flowmetry signals give information about many physiological activities of the cardiovascular system. The activities manifest themselves in rhythmic cycles. In order to explore these activities during the reactive hyperemia phenomenon, a novel time-frequency method, called the S-transform, based on a scalable Gaussian wavelet, is applied. The goal is to have a deeper understanding of reactive hyperemia. This paper focuses on the evaluation of the different activities between a rest signal and an hyperemia signal, both acquired simultaneously on the two forearms of healthy subjects. The results show that after the release of the occlusion, the myogenic, neurogenic, and endothelial related activities clearly increase on the forearm where the occlusion took place. Then, they return progressively to their basal level. However, on the rest forearm, no increase is noted for the three activities. The mechanisms that take place during reactive hyperemia are, therefore, local. The S-transform proves to be a suited time-frequency method, in order to analyze laser Doppler signal underlying mechanisms.

The interpretation of borehole images begins with the detection and classification of features—a time-consuming manual process subject to variations between interpreters. In seeking to automate the detection part for the most frequently picked features (which in circumferential images from clastic rock environments are sinusoids corresponding to planar or subplanar bedding surfaces and fractures), it is not necessary to pick all instances, but it is necessary to pick sufficient representative instances to satisfy the interpretation objective, accounting for a broad range of apparent dips, and allowing for the likelihood of fractures crossing bedding surfaces. A key challenge in this context is the minimization of false picks, as manual corrections would potentially negate the principal benefit of automation. A fast nonsubjective method is described for the detection of prominent discontinuities and the calculation of associated dip angles. It combines a gradient based approach for edge detection with a phase congruency method for validation, followed by a robust sinusoid detection technique. It has been evaluated on microresistivity images from wireline and logging-while-drilling tools, these images having a wide range of features with varying degrees of geologic complexity; the proportion of false positives in the case of noisy data is less than 5%, improving to better than 2% in the case of good-quality data. In contrast to manual picking, the method is fast and gives reproducible results. With potentially thousands of sinusoids in a single image, the method dramatically improves efficiency.

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Empirical Mode Decomposition (EMD) is a recently introduced tool for decomposing signals into so-called intrinsic mode functions (IMF). These IMF represent the data by means of oscillating waves with local zero mean. In some sense the decomposition can be compared with a time-varying filter bank, i.e., signals are decomposed using band limited filters with band widths that vary in time. The main attribute of EMD compared to other time-frequency tools is that it does not use any predetermined filters or transforms. It is therefore a self-contained method that preserves the physical properties in the separate IMF, explaining why it has been successfully applied in many engineering fields. This method is applied here on laser Doppler flowmetry signals and particularly on the hyperemia signals. Two interested hyperemia parameters are the maximum perfusion value and the corresponding time instant of appearance. Accurate values parameters are determined from the fifth IMF component. Computing these parameters allows us to improve diagnosis of some pathologies as peripheral arterial occlusive diseases.