Narrow Results

The wavelet transform seems particularly suited to analyse the electromyographic signal (EMG) during gait of asymptomatic and pathological subjects. Firstly, because physiologically the electrical activity generated by the muscles derives from a weighted sum of individual physiological components having limited support in time and in frequency. Secondly, because it is important to analyze muscle activity during specific phases of the cycle, and finally, because specific ranges of frequency are important pathological discriminators. In this paper we report the preliminary results of a project aimed at classifying asymptomatic and pathological subjects by analysing the complex wavelet transform of the EMG signal derived from two muscles (Tibialis Anterior and Lateral Gastrocnemius) during gait. An asymptomatic adult, an asymptomatic child and two pathological (cerebral palsy) children were examined using telemetric EMG devices and pressure footswitches. The results showed that the indices derived from the coefficient amplitudes (Gastrocnemius) and from frequency distribution (Tibialis) are capable of classifying the subjects into three groups. Despite the small number of cases analyzed, we believe that the relevance of the results deserves particular attention because of the novelty of the use of the wavelet transform for this application and of the potential application to monitor patients during interventions aimed at improving muscle behavior, particularly antispasticity treatment such as Botulinum Toxin injections.

In this work, an improvement of the Complex Wavelet Additive Synthesis (CWAS) algorithm is presented. This algorithm is based on a discrete version of the Complex Continuous Wavelet Transform (CCWT) which analyzes the input signal in a frame-to-frame approach and under variable frequency resolution per octave. After summarizing several Time-Frequency Distributions (TFD), concretely the standard Short Time Fourier Transform (STFT), the Pseudo Wigner–Ville Distribution (PWVD), reassignment and complex wavelets, a comparative study of the accuracy in the instantaneous frequency (IF) estimation is shown. The comparative study includes three different signal processing tools (based on the summarized TFD): the Time-Frequency Toolbox (TFTB) of François Auger, the High Resolution Spectrographic Routines (HRSR) of Sean Fulop and the proposed CWAS algorithm. A set of eight synthetic signals have been analyzed using six different methods: the regular STFT spectrogram, the PWVD, their corresponding reassigned versions, the Nelson crossed spectrum method and finally the Complex Continuous Wavelet Transform (CCWT). Finally, two- and three-dimensional Time-Frequency representations of the IF provided by the CWAS algorithm are presented.

In this paper, we propose a new hybrid Bivariate Complex Fourier Wavelet Regularized Deconvolution (Bi-ComForWaRD) that is an extension to the ComForWaRD algorithm, for medical imaging. This new algorithm is a two-step process, a global blur compensation using generalized Wiener filter and followed by a denoising algorithm using local adaptive Bivariate shrinkage function. It is a low-complexity denoising algorithm using the joint statistics of the wavelet coefficients and considers the statistical dependencies between the coefficients. And also, the performance of this system will be demonstrated on both the orthogonal wavelet transform and the dual-tree complex wavelet transform (DT-CWT) and some comparisons with the best available wavelet-based image denoising results will be given in order to illustrate the effectiveness of the system.