Appraisal of Nonpharmacological Chronic Pain Management

Chronic pain is a condition that impacts millions of men and women around the globe. It is a compelling disease that particularly impacts quality of life (QOL) for many veterans with undertreated or untreated pain. The focus of this systematic literature review was the appraisal of articles and clinical practice guidelines to better understand best-practice nonpharmacological strategies for management of chronic pain. Key words used in the literature search included chronic pain and veterans, complementary alternative medicine (yoga, tai chi, music therapy, acupuncture, and massage), and cognitive behavioral therapy (CBT). The articles included in the review were limited to those pertaining to adults over the age of 18 with non-cancer musculoskeletal chronic pain. The review excluded articles pertaining to patients reporting headache, cancer-related pain, fibromyalgia, mental health problems, or gynecological pain. Polit and Beck\u27s levels of evidence were used to appraise each article. The Stetler model was used as the change model for this project. Thirty-six articles met the criteria and were included. Nine clinical practice guidelines were appraised. Four articles were pilot studies, 3 met the criteria for Evidence Levels V-VII, 3 met the criteria for Levels III-IV, 8 were Level II, and 18 were systematic reviews of randomized controlled trials (Level I). The analysis of evidence supported the use of yoga, CBT, acupuncture, and massage therapy as best-practice methods of personalized nonpharmacological pain management. This project is important for those who care for veterans and other adult chronic pain patients. Application of the findings may lead to changes in chronic pain management that will enhance social change and improve QOL for veterans and others living with untreated or undertreated chronic pain

Sparse and Non-Negative BSS for Noisy Data

Non-negative blind source separation (BSS) has raised interest in various fields of research, as testified by the wide literature on the topic of non-negative matrix factorization (NMF). In this context, it is fundamental that the sources to be estimated present some diversity in order to be efficiently retrieved. Sparsity is known to enhance such contrast between the sources while producing very robust approaches, especially to noise. In this paper we introduce a new algorithm in order to tackle the blind separation of non-negative sparse sources from noisy measurements. We first show that sparsity and non-negativity constraints have to be carefully applied on the sought-after solution. In fact, improperly constrained solutions are unlikely to be stable and are therefore sub-optimal. The proposed algorithm, named nGMCA (non-negative Generalized Morphological Component Analysis), makes use of proximal calculus techniques to provide properly constrained solutions. The performance of nGMCA compared to other state-of-the-art algorithms is demonstrated by numerical experiments encompassing a wide variety of settings, with negligible parameter tuning. In particular, nGMCA is shown to provide robustness to noise and performs well on synthetic mixtures of real NMR spectra.Comment: 13 pages, 18 figures, to be published in IEEE Transactions on Signal Processin

Sparsity and adaptivity for the blind separation of partially correlated sources

Blind source separation (BSS) is a very popular technique to analyze multichannel data. In this context, the data are modeled as the linear combination of sources to be retrieved. For that purpose, standard BSS methods all rely on some discrimination principle, whether it is statistical independence or morphological diversity, to distinguish between the sources. However, dealing with real-world data reveals that such assumptions are rarely valid in practice: the signals of interest are more likely partially correlated, which generally hampers the performances of standard BSS methods. In this article, we introduce a novel sparsity-enforcing BSS method coined Adaptive Morphological Component Analysis (AMCA), which is designed to retrieve sparse and partially correlated sources. More precisely, it makes profit of an adaptive re-weighting scheme to favor/penalize samples based on their level of correlation. Extensive numerical experiments have been carried out which show that the proposed method is robust to the partial correlation of sources while standard BSS techniques fail. The AMCA algorithm is evaluated in the field of astrophysics for the separation of physical components from microwave data.Comment: submitted to IEEE Transactions on signal processin

Decomposition and dictionary learning for 3D trajectories

International audienceA new model for describing a three-dimensional (3D) trajectory is proposed in this paper. The studied trajectory is viewed as a linear combination of rotatable 3D patterns. The resulting model is thus 3D rotation invariant (3DRI). Moreover, the temporal patterns are considered as shift-invariant. This paper is divided into two parts based on this model. On the one hand, the 3DRI decomposition estimates the active patterns, their coefficients, their rotations and their shift parameters. Based on sparse approximation, this is carried out by two non-convex optimizations: 3DRI matching pursuit (3DRI-MP) and 3DRI orthogonal matching pursuit (3DRI-OMP). On the other hand, a 3DRI learning method learns the characteristic patterns of a database through a 3DRI dictionary learning algorithm (3DRI-DLA). The proposed algorithms are first applied to simulation data to evaluate their performances and to compare them to other algorithms. Then, they are applied to real motion data of cued speech, to learn the 3D trajectory patterns characteristic of this gestural language

Quaternionic Sparse Approximation

ISBN 978-0-8176-4267-9International audienceIn this paper, we introduce a new processing procedure for quaternionic signals through consideration of the well-known orthogonal matching pursuit (OMP), which provides sparse approximation. We present a quaternionic extension, the quaternionic OMP, that can be used to process a right-multiplication linear combination of quaternionic signals. As validation, this quaternionic OMP is applied to simulated data. Deconvolution is carried out and presented here with a new spikegram that is designed for visualization of quaternionic coefficients, and finally this is compared to multivariate OMP

About QLMS Derivations

International audienceIn this letter, a review of the quaternionic least mean squares (QLMS) algorithm is proposed. Three versions coming from three derivation ways exist: the original QLMS based on component wise gradients, the HR-QLMS based on a quaternion gradient operator and iQLMS based on an involutions-gradient. Noting and investigating the differences between the three QLMS formulations, we show that the original QLMS suffers from a mistake in the derivation calculus. Thus, we propose to derive rigorously the criterion following the first way, giving the correct version of QLMS. A comparison with the other QLMS versions validates these results on simulated data

Color Sparse Representations for Image Processing: Review, Models, and Prospects

International audienceSparse representations have been extended to deal with color images composed of three channels. A review of dictionary-learning-based sparse representations for color images is made here, detailing the differences between the models, and comparing their results on real data and simulated data. These models are considered in a unifying framework that is based on the degrees of freedom of the linear filtering/transformation of the color channels. Moreover, this allows it to be shown that the scalar quaternionic linear model is equivalent to constrained matrix-based color filtering, which highlights the filtering implicitly applied through this model. Based on this reformulation, the new color filtering model is introduced, using unconstrained filters. In this model, spatial morphologies of color images are encoded by atoms, and colors are encoded by color filters. Color variability is no longer captured in increasing the dictionary size, but with color filters, this gives an efficient color representation

Preprocessing for classification of sparse data: application to trajectory recognition

International audienceOn one hand, sparse coding, which is widely used in signal processing, consists of representing signals as linear combinations of few elementary patterns selected from a dedicated dictionary. The output is a sparse vector containing few coding coefficients and is called sparse code. On the other hand, Multilayer Perceptron (MLP) is a neural network classification method that learns non linear borders between classes using labeled data examples. The MLP input data are vectors, usually normalized and preprocessed to minimize the inter-class correlation. This article acts as a link between sparse coding and MLP by converting sparse code into convenient vectors for MLP input. This original association assures in this way the classification of any sparse signals. Experimental results obtained by the whole process on trajectories data and comparisons to other methods show that this approach is efficient for signals classification

Comparaison de descripteurs pour la classification de décompositions parcimonieuses invariantes par translation

National audienceNous étudions les descripteurs adaptés à la classification de décompositions parcimonieuses invariantes par translation. Nous comparons les différents descripteurs de l'état de l'art sur les mêmes données et avec le même classifieur, ce qui permet d'évaluer leurs efficacités et nous testons aussi leur robustesse à la translation. Grâce à un nouveau fenêtrage, une famille de nouveaux descripteurs est proposée, dépassant l'état de l'art tout en étant robuste à la translation

Ensemble learning for brain computer-interface using uncooperative democratic echo state communities

This paper deals with the issue of features construction and selection for signals acquired during non-invasive Brain-Computer Interface (BCI) experiments. The so-called Echo State Network (ESN) architecture, a reservoir computing approach proposed by H. Jaeger in 2001, is first adapted to the specific issue of EEG signals classification. In order to predict the performed task, a commonly used ESN architecture is combined with regularized logistic regression trained following aggressive subsampling principles. The resulting method is shown to significantly outperform classification rates obtained using raw EEG signals. Basic single ESNs are then integrated to take advantage of ensemble learning techniques and aggressive subsampling principles. The resulting new architecture, called Uncooperative Democratic Echo State Community (UDESC), constitutes one of the first attempt to provide an efficient subject-independent features construction algorithm. Based on the generative power of individual ESNs as well as the discriminative abilities of ensemble learning combined with aggressive subsampling, it is shown to advantageously integrate the knowledge acquired by each single ESN. The results shown along this paper make an extensive use of a real training dataset made available to the BCI community during BCI Competition 2008. This dataset consists of four subjects involved in a two-class motor-imagery BCI experiments