Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis

Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis

National Natural Science Foundation of China [81101115, 31200769]; Natural Science Foundation of Fujian Province of China [2011J01371]; Fundamental Research Funds for the Central Universities of China [2010121061]; Program for New Century Excellent Talents in Fujian Province UniversityHigh-resolution knee joint vibroarthrographic (VAG) signals can help physicians accurately evaluate the pathological condition of a degenerative knee joint, in order to prevent unnecessary exploratory surgery. Artifact cancellation is vital to preserve the quality of VAG signals prior to further computer-aided analysis. This paper describes a novel method that effectively utilizes ensemble empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA) algorithms for the removal of baseline wander and white noise in VAG signal processing. The EEMD method first successively decomposes the raw VAG signal into a set of intrinsic mode functions (IMFs) with fast and low oscillations, until the monotonic baseline wander remains in the last residue. Then, the DFA algorithm is applied to compute the fractal scaling index parameter for each IMF, in order to identify the anti-correlation and the long-range correlation components. Next, the DFA algorithm can be used to identify the anti-correlated and the long-range correlated IMFs, which assists in reconstructing the artifact-reduced VAG signals. Our experimental results showed that the combination of EEMD and DFA algorithms was able to provide averaged signal-to-noise ratio (SNR) values of 20.52 dB (standard deviation: 1.14 dB) and 20.87 dB (standard deviation: 1.89 dB) for 45 normal signals in healthy subjects and 20 pathological signals in symptomatic patients, respectively. The combination of EEMD and DFA algorithms can ameliorate the quality of VAG signals with great SNR improvements over the raw signal, and the results were also superior to those achieved by wavelet matching pursuit decomposition and time-delay neural filter

An artificial-neural-network-based multiple classifier system for knee-joint vibration signal classification

Conference Name:2011 International Conference on Computer, Communication, Control and Automation, 3CA 2011. Conference Address: Zhuhai, China. Time:November 19, 2011 - November 20, 2011.Asia Pacific Human-Computer Interaction Research Centre (APHCI)The knee-joint vibration or vibroarthrographic (VAG) signal could be used as an indicator with regard to the condition of degenerative articular cartilage surfaces of the knee joint. Analysis of VAG signals can assist in the screening for knee-joint pathology and help prevent unnecessary exploratory surgery. This paper proposes a multiple classifier system (MCS) based on artificial neural networks for the classification of VAG signals with statistical features. The multiple classifier system combines a group of component least-squares support vector machine classifiers with a linear and normalized fusion model. The fusion model minimizes the mean-squared error (MSE) of the MCS by solving the corresponding constrained quadratic programming problem, and the optimal weights are derived from the energy convergence process of a recurrent neural network. The results obtained with a data set of 89 VAG signals show that the proposed MCS can effectively reduce the classification error in terms of MSE. In addition, the proposed MCS also provides an area of 0.8230 under the receiver operating characteristics curve, which is much better in comparison with any one of the component networks with different input features, and also superior to the popular simple average or weighted average fusion method. ? 2011 Springer-Verlag

Chondromalacia Patellae Detection by Analysis of Intrinsic Mode Functions in Knee Joint Vibration Signals

Conference Name:World Congress on Medical Physics and Biomedical Engineering. Conference Address: Beijing, China. Time:May 26, 2012 - May 31, 2012.This paper presents the knee joint vibration signal processing and pathological localization procedures using the empirical mode decomposition for patients with chondromalacia patellae. The artifacts of baseline wander and random noise were identified in the decomposed monotonic trend and intrinsic mode functions, using the probability density function modeling method and the confidence limit criterion. Then, the fluctuation parts in the signal were detected by the signal turns count method. The results demonstrated that the quality of reconstructed signal can be greatly improved, with the removal of the baseline wander (adaptive trend) and the Gaussiandistributed random noise. By detecting the signal turns in the artifact-free signal, the pathological segments related to chondromalacia patellae can be effectively localized with the beginning and ending points of the span of signal turns. ? 2013 Springer-Verlag

Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis

Le suicide en milieu pénitentiaire (état des lieux et enquête préliminaire sur la formation du personnel)

Le suicide est un problème de santé publique, tant en population générale qu en milieu carcéral. En prison, le nombre des suicides augmente significativement depuis plusieurs années. Ainsi, l Administration pénitentiaire et les Ministères de la Santé et de la Justice se sont saisis du problème, avec la parution de deux rapports en 2004 et en 2009. Leur mission était d évaluer et de proposer un programme de prévention du suicide des personnes détenues. Ils concluent à une série de recommandations, dont un des axes principaux est la formation spécifique de l ensemble du personnel intervenant en milieu pénitentiaire. En Juin 2012, nous avons réalisé une enquête sur la formation spécifique à la prévention du suicide des personnels intervenant en prison, grâce à un questionnaire, qui a été distribué à l ensemble du personnel de la Maison d Arrêt de Grenoble-Varces. Nos résultats confirment l hypothèse de départ du manque de formation spécifique des personnes travaillant en prison. En effet, moins de la moitié du personnel a reçu cette formation. De plus, certaines personnes y ont assisté il y a plus de dix ans, et aurait besoin d un rappel de formation. Au total, la moitié du personnel ressent un besoin de formation complémentaire, et ne se sent pas bien formé en tant qu acteur de la prévention du suicide. Cependant, les données épidémiologiques (facteurs de risque, périodes à risque et moyens de suicide) sont connues, ce qui est rassurant. Finalement, huit ans après le premier rapport ministériel, on constate que les objectifs prédéfinis en termes de formation des intervenants ne sont pas atteints.Suicide is a real public health problem, by the loss of life it provoked and by the psychological and social problems as reflected in it. It is always a painful event, which returns to the guilt of loved ones and the responsibility of those present. In prison, the number of suicides increased significantly for several years. The Prison Administration and the Ministries of Health and Justice are dealing with the problem. Two reports were published in 2004 and 2009. Their mission was to assess and propose a program for the prevention of suicide of persons detained. These two reports conclude with a series of recommendations. One of the principal axes is the specific training of all staff involved in the prison environment. In June 2012, we conducted an investigation in order to evaluate the specific training of personnel involved in prison suicide prevention. It is a questionnaire of self-evaluation, distributed to all the staff of the house arrest of Grenoble - Varces. The results confirm the hypothesis of the lack of specific training of persons working in prison. Indeed, less than half of the staff received this training. In addition, some people received this training over ten years ago, and would need a reminder. In total, half of the staff feels a need for further training, and feels not well trained as an actor in the prevention of suicide. However, the answers concerning the epidemiological data are reassuring. Suicide risk factors and periods most at risk are identified by stakeholders. Similarly, the means of suicide the most used are known. Finally, eight years after the first report, we found that the targets pre-defined in terms of training of stakeholders are not achieved.GRENOBLE1-BU Médecine pharm. (385162101) / SudocSudocFranceF

Enophtalmie de la cavité anophtalme ((à propos de deux séries de pathologie acquise))

Le problème de l énophtalmie chez le sujet anophtalme est avant tout un défi d évaluation à la fois clinique, objectif et d imagerie. Une première série est constituée de 86 patients opérés au CHU d Amiens, et réhabilités par prothèse. Elle servira de modèle pour asseoir le diagnostic d énophtalmie de la cavité anophtalme. Sont ainsi dégagés des outils d évaluation photographique qui permettent la description des types morphologiques orbitaires, ainsi qu un score clinique d énophtalmie. A l issue de ce chapitre, une approche géométrique de l énophtalmie sera dégagée attestant du rôle prépondérant de la morphologie orbitaire sur le type d énophtalmie, puisque la perte de volume du contenu se répartit sur l ensemble de la surface orbito-palpébrale qui sera plus large dans le cadre ouvert et plus concentrée sur l axe antéro-postérieur dans le cadre fermé. Au terme des possibilités d une réhabilitation primaire de l anophtalme par les techniques prothétiques, le rôle du chirurgien puisant dans toutes les techniques de reconstruction du contenu et contenant orbitaire sera d améliorer la stabilité de la prothèse et corriger l énophtalmie résiduelle. C est l objet de la seconde série de patients suivis par le Docteur Sorrel-Déjerine (Paris). 34 patients ont été pris en charge sur une cohorte de 50 demandeurs. L analyse détaillée de chacun de ces cas conduit à établir une démarche chirurgicale faisant l objet d un algorithme décisionnel. Les différents types de correction sont exposés et illustrés par quelques cas qui mettent en exergue la difficulté d appréciation objective du volume à restituer et de sa topographie exacte. Aussi, nous utiliserons au mieux les techniques d imagerie (scanner et IRM) pour l évaluation de ce volume manquant dans l espace orbitaire, avec l élaboration d un protocole d étude volumétrique des différents compartiments de la cavité. Reste la part surfacique oculopalpébrale qui est ici étudiée par photogrammétrie, acquisition tridimensionnelle d image surfacique par balayage laser, avec le double avantage de l innocuité et du meilleur rendu morphologique réaliste, comparatif, et quantitatif. L étape ultérieure est celle de la modélisation orbitaire qui prendra en compte la dynamique des volumes autour de l œil en suspension dans l orbite et renseignera davantage sur la physiopathologie du syndrome de l énucléé. Ainsi les acteurs de la prise en charge de l énophtalmie chez l anophtalme disposeront des outils leur permettant à chaque étape une analyse objective de la cavité anophtalme et de ses modifications, le but étant d améliorer la prise en charge en chirurgie de première intention (choix de l'implant) mais également en chirurgie secondaire correctrice après planification de l augmentation des volumes.Enophtalmos management is a real challenge of clinical and radiological analysis. A first study is composed of 86 patients who have undergone ocular socket s reconstruction with orbital implant and prosthesis, at the university hospital of Amiens. It gives the opportunity to create a classification of different morphological type of orbit and a clinical score of enophtalmos degree, that become precious devices for photographic analysis. In a geometrical approach, the morphological type explains the occurrence and the severity of enophtalmos. The volume loss is visible on the entire orbitopalpebral surface of the socket that is more or less exposed depending on this type : wider on an opened and large orbital shape but narrowed around the anteroposterior axis on a closed shape. After primary surgery followed by achievement of prothesis rehabilitation, the surgeon s role is to explore the different surgical procedures available involving the bone container or the soft tissue content to provide prosthesis stability and to treat the residual enophtalmos. That s the subject of a second study composed of 34 patients operated by Dr Sorrel-Déjerine in Paris. The detailed analysis lead to the development of a surgical strategy that is synthesized on a algorithm. The correction procedures are exposed and illustrated by examples that highlight the difficulty to assess with objectivity the missing volume to return and it s topography. So, we take advantage of the imagery s techniques (CT scan, MRI...) to evaluate this volume in the orbital space. For this purpose, a protocol has been conceived to locate the volume loss in the different orbital compartments. But for an evaluation of the oculopalpebral surface, the photogrammetry is a precious realistic device to situate the visible defect in 3D by comparison between the two sides. The next step involves the analysis model of orbital biomechanics that simulate the volumes moving in the orbital cavity for a better anderstanding of the anophtalmic socket s physiopathology. Those devices are exposed here to help the specialist concerned in enophtalmos treatment of anophtalmic patients, to do an objectiv analysis with a surgical planification at each step of correction. The purpose is to provide the best choice of procedure in primary care and secondary volume enhancement surgery.AMIENS-BU Santé (800212102) / SudocSudocFranceF

Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with Parkinson's disease.

Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson's disease (PD), and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS) and kernel principal component analysis (KPCA) methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher's linear discriminant analysis (FLDA) was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP) decision rule and support vector machine (SVM) with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC) curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified

Detrending knee joint vibration signals with a cascade moving average filter

Conference Name:34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012. Conference Address: San Diego, CA, United states. Time:August 28, 2012 - September 1, 2012.IEEE EMB; IEEE CAS; IEEE SMC; SONNETKnee joint vibration signals are very useful for computer-aided analysis of the pathological conditions in the knee. In a vibration arthrometry test, the legs of patients with knee joint disorders may tremble due to the reaction of pain, which causes the baseline wander that may affect the diagnostic decision making in medical study. This paper presents a new type of cascade moving average filter with hierarchical layers to remove the baseline wander in the raw knee joint vibration signals. The first layer of the cascade filter contains two moving averaging operators with the same order. The five tail inputs of the first moving averaging operator are overlapping with the beginning inputs of the successive operator. The piecewise linear trends estimated by the moving average operators in the first layer were smoothed in the final cascade filter output. The simulation results showed that the cascade filter can effectively remove the baseline wander in the raw knee joint vibration signals. 漏 2012 IEEE