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Abstract: Currently, the circadian timing system of cancer patients can be estimated with wrist actimetry, which provides numerical data on the continuous activity of patients. The method of processing of these data, which we implemented, aims at answering the following questions: is the circadian timing system stable before chemotherapy, is this system extensively modified by chemotherapy, are there structural modifications or a decrease in activity amplitude produced by chemotherapy and how does the circadian activity rhythm recover? Here, we determine a circadian model function using a wavelet transform prior to chemotherapy delivery. Then we measure the correlation between this model function and the evolution of the circadian rhythm of activity over the whole observation span during and after chemotherapy. The application of this method to the record of activity rhythms in cancer patients highlights the characteristics and the recovery pattern of chemotherapy-induced alterations of the circadian timing system. [Copyright &y& Elsevier]

This paper introduces the use of wavelet analysis to follow the temporal variations in the coupling between oscillatory neural signals. Coherence, based on Fourier analysis, has been commonly used as a first approximation to track such coupling under the assumption that neural signals are stationary. Yet, stationary neural processing may be the exception rather than the rule. In this context, the recent application to physical systems of a wavelet-based coherence, which does not depend on the stationarity of the signals, is highly relevant. This paper fully develops the method of wavelet coherence and its statistical properties so that it can be practically applied to continuous neural signals. In realistic simulations, we show that, in contrast to Fourier coherence, wavelet coherence can detect short, significant episodes of coherence between non-stationary neural signals. This method can be directly applied for an ‘online’ quantification of the instantaneous coherence between two signals. [ABSTRACT FROM AUTHOR]

This paper is aimed at presenting the two main classes of nonstationary signal transforms that are currently used to analyze and to characterize EEG observations. Time-scale methods, or wavelet transforms, allow a time versus duration analysis to be performed whereas time-frequency methods allow spectral contents to be analyzed as a function of time. These two types of transform are well suited to the study of changes either localized or progressive that may be observed in EEG signal dynamics and that sign the evolution of underlying physiological mechanisms. The potential interest of these methods in nonstationary signal representation is illustrated through several academic examples. Then, methods are applied on real EEG signals to solve problems such that the detection of interictal transient signals (like spikes or spike-waves) and the recognition of signatures during ictal periods. [ABSTRACT FROM AUTHOR]

Interindividual variability among a population induces some chronobiological analysis difficulties; to solve them we create a so called Model Function, which is associated to a biological rhythm. This model function is an image of a population and can be used directly for further studies. Its building has several steps: normalisation, wavelet transform, threshold application, summation of transforms and inverse wavelet transform. This algorithm is tested on activity rhythm of four inbred mouse strains: B, C, BC and CB; this has permitted to check that F1s have an heterotic effect and that this acrophasis indicates a B dominance. The Model Function permits to deal with some difficult problems, such as rhythm shape and position of a population acrophasis. Those elements are absolutely necessary in chronotherapy which needs the knowledge of maximum efficacy and toxicity hours. [Copyright &y& Elsevier]