Amar Kachenoura

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Glossary x vector of components x p , 1 ≤ p ≤ P s, x, y sources, observations, separator outputs N number of sources P number of sensors T number of observed samples convolution A matrix with components A ij A, B mixing and separation matrices G, W, Q global, whitening, and separating unitary matriceš g Fourier transform of g s estimate of quantity s p x(More)
Several studies dealing with independent component analysis (ICA)-based brain-computer interface (BCI) systems have been reported. Most of them have only explored a limited number of ICA methods, mainly FastICA and INFOMAX. The aim of this article is to help the BCI community researchers, especially those who are not familiar with ICA techniques, to choose(More)
Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic(More)
Independent Component Analysis (ICA) plays an important role in biomedical engineering. Indeed, the complexity of processes involved in biomedicine and the lack of reference signals make this blind approach a powerful tool to extract sources of interest. However, in practice, only few ICA algorithms such as SOBI, (extended) InfoMax and FastICA are used(More)
This paper deals with the conception of a new system for sleep staging in ambulatory conditions. Sleep recording is performed by means of five electrodes: two temporal, two frontal and a reference. This configuration enables to avoid the chin area to enhance the quality of the muscular signal and the hair region for patient convenience. The(More)
Empirical Mode Decomposition (EMD) is an emerging topic in signal processing research, applied in various practical fields due in particular to its data-driven filter bank properties. In this paper, a novel EMD approach called X-EMD (eXtended-EMD) is proposed, which allows for a straightforward decomposition of monoand multivariate signals without any(More)
Current cardiac implantable devices offer improved processing power and recording capabilities. Some of these devices already provide basic telemonitoring features that may help to reduce health care expenditure. A challenge is posed in particular for the telemonitoring of the patient’s cardiac electrical activity. Indeed, only intracardiac electrograms(More)
OBJECTIVE We propose a new method for automatic detection of fast ripples (FRs) which have been identified as a potential biomarker of epileptogenic processes. METHODS This method is based on a two-stage procedure: (i) global detection of events of interest (EOIs, defined as transient signals accompanied with an energy increase in the frequency band of(More)
The main challenge in prostate cancer radiotherapy is to deliver the prescribed dose to the clinical target while minimizing the dose to the neighboring organs at risk and thus avoiding subsequent toxicity-related events. With the aim of improving toxicity prediction following prostate cancer radiotherapy, the goal of our work is to propose a new predictive(More)
A novel empirical mode decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this correspondence. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new(More)