Processing Polysomnographic Signals, using Independent Component Analysis

International audienceIn this paper several applications of the Independent Component Analysis (ICA) algorithm, for the analysis of biomedical signal recordings have been investigated. One of these applications is the removal of EEG artifacts such as the EOG. It is shown that ICA may serve as a powerful tool, which could help the analysis of biomedical recordings, and give better insights about the underlying sources of some disorders. Another application of the proposed method is the detection of sleep disorders in patients suffering from sleep apnea. The ultimate goal of this approach is to develop an automatic noninvasive data acquisition system, for clinical applications

Various Ways to Compute the Continuous-Discrete Extended Kalman Filter

International audienceThe Extended Kalman Filter (EKF) is a very popular tool dealing with state estimation. Its continuous-discrete version (CD-EKF) estimates the state trajectory of continuous-time nonlinear models, whose internal state is described by a stochastic differential equation and which is observed through a noisy nonlinear form of the sampled state. The prediction step of the CD-EKF leads to solve a differential equation that cannot be generally solved in a closed form. This technical note presents an overview of the numerical methods, including recent works, usually implemented to approximate this filter. Comparisons of theses methods on two different nonlinear models are finally presented. The first one is the Van der Pol oscillator which is widely used as a benchmark. The second one is a neuronal population model. This more original model is used to simulate EEG activity of the cortex. Experiments showed better stability properties of implementations for which the positivity of the prediction matrix is guaranteed

Extraction des dynamiques du système nerveux autonome : Une approche basée sur la séparation aveugle de sources.

International audienceThis paper presents a method to estimate the Sympathetic and Parasympathetic Autonomic Nervous System components (SNAS and SNAP respectively) by using an indicator obtained from surface ECGs. The long-term goal of the project is to quantitatively analyze the ratio between SNAS and SNAP in order to better characterize certain pathologies. An approach based on Independent Component Analysis (ICA), which exploits the di erent activation delays of SNAS and SNAP is proposed and applied to a database with normal subjects and diabetic patients. Preliminary results show a good separation, in the frequency domain, of SNAS and SNAP dynamics, including a di erentiation of SNAS and SNAP in the low-frequency band. Results are promising for a better quanti cation of the autonomic state of the patients

A fast algorithm for the computation of 2-D forward and inverse MDCT

International audienceA fast algorithm for computing the two-dimensional (2-D) forward and inverse modified discrete cosine transform (MDCT and IMDCT) is proposed. The algorithm converts the 2-D MDCT and IMDCT with block size M N into four 2-D discrete cosine transforms (DCTs) with block size ðM=4Þ ðN=4Þ. It is based on an algorithm recently presented by Cho et al. [An optimized algorithm for computing the modified discrete cosine transform and its inverse transform, in: Proceedings of the IEEE TENCON, vol. A, 21–24 November 2004, pp. 626–628] for the efficient calculation of onedimensional MDCT and IMDCT. Comparison of the computational complexity with the traditional row–column method shows that the proposed algorithm reduces significantly the number of arithmetic operations

An Alternating Direction Method of Multipliers for Constrained Joint Diagonalization by Congruence (Invited Paper)

International audienceIn this paper, we address the problem of joint diagonalization by congruence (i.e. the canonical polyadic decomposition of semi-symmetric 3rd order tensors) subject to arbitrary convex constraints. Sufficient conditions for the existence of a solution are given. An efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) is then designed. ADMM provides an elegant approach for handling the additional constraint terms, while taking advantage of the structure of the objective function. Numerical tests on simulated matrices show the benefits of the proposed method for low signal to noise ratios. Simulations in the context of nuclear magnetic resonance spectroscopy are also provided

Comparison of three spike detectors dedicated to single unit action potentials of the auditory nerve

International audienceThis paper compares three methods for the detection of single unit action potentials in auditory nerve. The detector structures are similar consisting of a filtering procedure in the first stage and a decision rule in the second stage. The detection accuracy of each detector is characterized by the couple probability of a true detection vs. rates of false detection with synthetic data. The performance comparison between detectors shows that the detector using a band-pass finite-impulse-response filter with complex coefficients offers the best performance. This observation was especially evident for low signal to noise ratios. This finding is confirmed with real data and leads us to revise the protocol of spike detection in auditory nerve