A simple method to assess freezing of gait in Parkinson's disease patients

Freezing of gait (FOG) can be assessed by clinical and instrumental methods. Clinical examination has the advantage of being available to most clinicians; however, it requires experience and may not reveal FOG even for cases confirmed by the medical history. Instrumental methods have an advantage in that they may be used for ambulatory monitoring. The aim of the present study was to describe and evaluate a new instrumental method based on a force sensitive resistor and Pearson's correlation coefficient (Pcc) for the assessment of FOG. Nine patients with Parkinson's disease in the "on" state walked through a corridor, passed through a doorway and made a U-turn. We analyzed 24 FOG episodes by computing the Pcc between one "regular/normal" step and the rest of the steps. The Pcc reached +/- 1 for "normal" locomotion, while correlation diminished due to the lack of periodicity during FOG episodes. Gait was assessed in parallel with video. FOG episodes determined from the video were all detected with the proposed method. The computed duration of the FOG episodes was compared with those estimated from the video. The method was sensitive to various types of freezing; although no differences due to different types of freezing were detected. The study showed that Pcc analysis permitted the computerized detection of FOG in a simple manner analogous to human visual judgment, and its automation may be useful in clinical practice to provide a record of the history of FOG

The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia

Dementia being a syndrome caused by a brain disease of a chronic or progressive nature, in which the irreversible loss of intellectual abilities, learning, expressions arises; including memory, thinking, orientation, understanding and adequate communication, of organizing daily life and of leading a family, work and autonomous social life; leads to a state of total dependence; therefore, its early detection and classification is of vital importance in order to serve as clinical support for physicians in the personalization of treatment programs. The use of the electroencephalogram as a tool for obtaining information on the detection of changes in brain activities. This article reviews the types of cognitive spectrum dementia, biomarkers for the detection of dementia, analysis of mental states based on electromagnetic oscillations, signal processing given by the electroencephalogram, review of processing techniques, results obtained where it is proposed the mathematical model about neural networks, discussion and finally the conclusions

Compression of physiological signals

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Joint estimation of dynamics and shape of physiological signals through genetic algorithms

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Biosignal: Acquisition and general properties

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QRS complex detection using Empirical Mode Decomposition

International audienceIn this paper, we present a new Empirical Mode Decomposition based algorithm for the purpose of QRS complex detection. This algorithm requires the following stages: a high-pass filter, signal Empirical Mode Decomposition, a nonlinear transform, an integration and finally, a low-pass filter is used. In order to evaluate the proposed technique, the well known ECG MIT–BIH database has been used. Moreover it is compared to a reference technique, namely “Christov's” detection method. As it will be shown later, the proposed algorithm allows to achieve high detection performances, described by means both the sensitivity and the specificity parameters

Recalage d’images d’empreintes digitales en biométrie sans contact

Parmi toutes les données biométriques, les empreintes digitales sont les caractéristiques humaines les plus commodes, largement utilisées pour identifier les individus. Cependant, les systèmes d’acquisition des empreintes digitales basés sur le contact présentent des inconvénients liés à l’élasticité de la peau, l’inconsistance des placements, les conditions de l’environnement, etc. Dans cet article, nous présentons une approche de recalage d’images d’empreintes digitales acquises sans contact. Pour estimer la transformation géométrique, on recherche un ensemble de paires de points dans les images à apparier. Ces paires sont construites à partir de points d’intérêt extraits des images à l’aide du détecteur de Harris. La correspondance entre les ensembles de points de contrôle, est obtenue en calculant le vecteur descripteur des moments de Zernike sur une fenêtre circulaire centrée en chaque point. Les moments de Zernike sont calculés sur la représentation en niveau de gris de ces images. La comparaison des coefficients de corrélation entre les vecteurs descripteurs des moments de Zernike, permet de définir les points homologues. L’estimation des paramètres de la déformation existante entre les images est effectuée en utilisant l’algorithme RANSAC (RANdomSAmple Consensus) qui supprime les fausses correspondances. Nous illustrons la méthode proposée sur une base de 100 images que nous avons constituées.Mots clés : Biométrie sans contact, Empreinte digitale, Moments de Zernike, Détecteur de Harris, Recalage.English AbstractFor all data biometry, the fingerprints are the most convenient human features, extensively used in the goal to identify the individuals by contact. This counterpart the fingerprint systems based on contact have major inconveniences bound to the investment of the finger, to the pressure exercised on the sensor. In this paper, we present an approach for the registration of fingerprint images acquired without contact. We using a set of interest points extracted from the images with the Harris detector to estimate the geometric transformation. The correspondence between the sets control points, is obtained by calculating the descriptor vector of Zernike moments on a circular window centered at each point. Zernike moments are calculated on the grayscale representation of these images. Comparison of correlation coefficients between the vectors of Zernike moments descriptors, used to define the corresponding points. The estimation of parameters of the existing deformation between the images is performed using the RANSAC algorithm (Random SAmple Consensus) that suppresses false matches. The proposed algorithm has been tested on a set of 100 fingerprint images.Keywords: Contactless Biometry, Fingerprint, Zernike moments, Harris detector, Image registration

Neural networks approaches for EEG classification

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Compression of dynamic and volumetric medical data sets

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