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Symptoms of psychological distress and disorder have been widely reported in people under quarantine during the COVID-19 pandemic;in addition to severe disruption of peoples’ daily activity and sleep patterns This study investigates the association between physical-activity levels and sleep patterns in quarantined individuals An international Google online survey was launched in April 6th, 2020 for 12-weeks Forty-one research organizations from Europe, North-Africa, Western-Asia, and the Americas promoted the survey through their networks to the general society, which was made available in 14 languages The survey was presented in a differential format with questions related to responses “before” and “during” the confinement period Participants responded to the Pittsburgh Sleep Quality Index (PSQI) questionnaire and the short form of the International Physical Activity Questionnaire 5056 replies (59 4% female), from Europe (46 4%), Western-Asia (25 4%), America (14 8%) and North-Africa (13 3%) were analysed The COVID-19 home confinement led to impaired sleep quality, as evidenced by the increase in the global PSQI score (4 37 ± 2 71 before home confinement vs 5 32 ± 3 23 during home confinement) (p

Artificial Neural Networks (ANNs) are being widely used in supervised Machine Learning (ML) to analyze signals or images for many applications. Using an annotated learning database, one of the main challenges is to optimize the network weights. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively such as gradient-based method, Newton-type method, meta-heuristic method. Moreover, sparse regularizer is designed to zero superfluous weights and hence remove unneeded connections. However, if one wants to promote sparse networks, such as the 1 norm, one need use sparse regularizations, the optimization process becomes challenging since the error to be minimized is no longer differentiable. In this paper, we propose an MCMC-based optimization scheme formulated in a Bayesian framework. The proposed scheme solves the above-mentioned sparse optimization problem using an efficient sampling scheme and Hamiltonian dynamics. The designed optimizer is conducted on four datasets, two COVID-19 and two standard datasets (Fashion-MNIST and CIFAR-10). The results are verified by a comparative study with three different CNNs, one of which is deeper than the others in order to evaluate the effectiveness of our optimizer. Promising results show the usefulness of the proposed method to allow ANNs, even with low complexity levels, reaching high accuracy rates of up to 94% in most of the experiments carried out. The proposed method is also faster and more robust concerning overfitting issues. More importantly, the training step of the proposed method is much faster than all competing algorithms.

Background. The COVID-19 lockdown could engender disruption to lifestyle behaviors, thus impairing mental wellbeing in the general population. This study investigated whether sociodemographic variables, changes in physical activity, and sleep quality from pre- to during lockdown were predictors of change in mental wellbeing in quarantined older adults. Methods. A 12-week international online survey was launched in 14 languages on 6 April 2020. Forty-one research institutions from Europe, Western-Asia, North-Africa, and the Americas, promoted the survey. The survey was presented in a differential format with questions related to responses “pre” and “during” the lockdown period. Participants responded to the Short Warwick–Edinburgh Mental Wellbeing Scale, the Pittsburgh Sleep Quality Index (PSQI) questionnaire, and the short form of the International Physical Activity Questionnaire. Results. Replies from older adults (aged &gt, 55 years, n = 517), mainly from Europe (50.1%), Western-Asia (6.8%), America (30%), and North-Africa (9.3%) were analyzed. The COVID-19 lockdown led to significantly decreased mental wellbeing, sleep quality, and total physical activity energy expenditure levels (all p &lt, 0.001). Regression analysis showed that the change in total PSQI score and total physical activity energy expenditure (F(2, 514) = 66.41 p &lt, 0.001) were significant predictors of the decrease in mental wellbeing from pre- to during lockdown (p &lt, 0.001, R2: 0.20). Conclusion. COVID-19 lockdown deleteriously affected physical activity and sleep patterns. Furthermore, change in the total PSQI score and total physical activity energy expenditure were significant predictors for the decrease in mental wellbeing.

International audience; Drowsiness detection is still an open issue, especially when detection is based on physiological signals. In this sense, light non-invasive modalities such as electroencephalography (EEG) are usually considered. EEG data provides informations about the physiological brain state, directly linked to the drowsy state. Electrocardigrams (ECG) signals can also be considered to involve informations related to the heart state. In this study, we propose a method for drowsiness detection using joint EEG and ECG data. The proposed method is based on a deep learning architecture involving convolutional neural networks (CNN) and recurrent neural networks (RNN). High efficiency level is obtained with accuracy scores up to 97% on validation set. We also demonstrate that a modification of the proposed architecture by adding autoencoders helps to compensate the performance drop when analysing subjects whose data is not presented during the learning step.

Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures, (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

International audience; Spectral unmixing allows to extract endmembers and estimate their proportions in hyperspectral data. Each observed pixel is considered to be a linear combination of several endmembers spectra. Based on a novel hierarchical Bayesian model, change detection into hyperspectral images is achieved by unmixing. A Gibbs sampler is proposed to overcome the complexity of integrating the resulting posterior distribution. The performance of the proposed Bayesian change detection method is evaluated on real data. It provides binary detection with a precision rate up to 98.90%.

BackgroundPublic health recommendations and governmental measures during the COVID-19 pandemic have enforced numerous restrictions on daily living including social distancing, isolation and home confinement. While these measures are imperative to mitigate spreading of COVID-19, the impact of these restrictions on psychosocial health is undefined. Therefore, an international online survey was launched in April 2020 in seven languages to elucidate the behavioral and lifestyle consequences of COVID-19 restrictions. This report presents the preliminary results from the first thousand responders on social participation and life satisfaction.MethodsThirty-five research organisations from Europe, North-Africa, Western Asia and the Americas promoted the survey through their networks to the general society, in English, German, French, Arabic, Spanish, Portuguese, and Slovenian languages. Questions were presented in a differential format with questions related to responses “before” and “during” confinement conditions.Results1047 replies (54% women) from Asia (36%), Africa (40%), Europe (21%) and other (3%) were included in the analysis. Preliminary findings revealed psychosocial strain during the enforced COVID-19 home confinement. In particular, large decreases in the amount of social activity through family (58%), friends/neighbors (44.9%) or entertainment (46.7%) were triggered by the enforced confinement. These negative effects on social participation were also associated with lower satisfaction (−30.5%) during the confinement period. Conversely, social contact score through digital technologies has significantly increased (pConclusionThese preliminary findings elucidate the risk of psychosocial strain during the current home confinement period. Therefore, in order to mitigate the negative psychosocial effects of home confinement, implementation of national strategies focused on promoting social inclusion through technology-based solution is urgently needed.

Aims: Prognosis of lung mathology severity after Covid-19 infection using chest X-ray time series Background: We have been inspired by methods analysing time series of images in remote sensing for change detection. During the current Covid-19 pandemic, our motivation is to provide an automatic tool to predict severity of lung pathologies due to Covid-19. This can be done by analysing images of the same patient acquired at different dates. Since no analytical model is available, and also no accurate quantification tools can be used due to many unknowns about the pathology, feature-free methods are good candidates to analyse such temporal images. Objective: This contribution helps improving performances of medical structures facing the Covid-19 pandemic. The first impact is medical and social since more lives could be saved with a 92% rate of good prognosis. In addition to that, patients in intensive care units (up to 15%) could a posteriori suffer from less sequels due to an early and accurate prognosis of their PP. Moreover, accurate prognosis can lead to a better planning of patient’s transfer between units and hospitals, which is linked to the second claimed economical impact. Indeed, prognosis is linked to lower treatment costs due to an optimized predictive protocol using ragiological prognosis. Methods: Using Convolutional Neural Networks (CNN) in combination with Recurrent Neural Networks (RNN). Spatial and temporal features are combines to analyse image time series. A prognosis score is delivered indicating the severity of the pathology. Learning is made on a publicly available database. Results: When applied to radiological time-series, promising results are obtained with an accuracy rates higher than 92%. Sensitivity and specificity rates are also very interesting. Conclusion: Our method is segmentation-free, which makes it competitive with respect to other assessment methods relying on time-consuming lung segmentation algorithms. When applied on radiographic data, the proposed ProgNet architecture showed promising results with good classification performances, especially for ambiguous cases. Specifically, the reported low false positive rates are interesting for an accurate and personalised care workflow.

BackgroundPublic health recommendations and governmental measures during the COVID-19 pandemic have enforced numerous restrictions on daily living including social distancing, isolation and home confinement. While these measures are imperative to abate the spreading of COVID-19, the impact of these restrictions on health behaviours and lifestyle at home is undefined. Therefore, an international online survey was launched in April 2020 in seven languages to elucidate the behavioral and lifestyle consequences of COVID-19 restrictions. This report presents the preliminary results from the first thousand responders on physical activity (PA) and nutrition behaviours.MethodsThirty-five research organisations from Europe, North-Africa, Western Asia and the Americas promoted the survey through their networks to the general society, in English, German, French, Arabic, Spanish, Portugese, and Slovenian languages. Questions were presented in a differential format with questions related to responses “before” and “during” confinement conditions.Results1047 replies (54% women) from Asia (36%), Africa (40%), Europe (21%) and other (3%) were included into a general analysis. The COVID-19 home confinement had a negative effect on all intensities of PA (vigorous, moderate, walking and overall). Conversely, daily sitting time increased from 5 to 8 hours per day. Additionally, food consumption and meal patterns (the type of food, eating out of control, snacks between meals, number of meals) were more unhealthy during confinement with only alcohol binge drink decreasing significantly.ConclusionWhile isolation is a necessary measure to protect public health, our results indicate that it alters physical activity and eating behaviours in a direction that would compromise health. A more detailed analysis of survey data will allow for a segregation of these responses in different age groups, countries and other subgroups which will help develop bespoke interventions to mitigate the negative lifestyle behaviors manifest during the COVID-19 confinement.

BackgroundPublic health recommendations and government measures during the COVID-19 pandemic have enforced restrictions on daily living, which may include social distancing, remote work/school, and home confinement. While these measures are imperative to abate the spreading of COVID-19, the impact of these restrictions on mental health and emotional wellbeing is undefined. Therefore, an international online survey was launched on April 6, 2020 in seven languages to elucidate the impact of COVID-19 restrictions on mental health and emotional well-being. This report presents the preliminary results from the first thousand responders on mental wellbeing and mood and feelings questionnaires.MethodsThe ECLB-COVID19 electronic survey was designed by a steering group of multidisciplinary scientists and academics, following a structured review of the literature. The survey was uploaded and shared on the Google online survey platform. Thirty-five research organizations from Europe, North-Africa, Western Asia and the Americas promoted the multi-languages survey through their networks to general society. Of the 64 questions, 7 were from the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS), and 13 were from the Short Mood and Feelings Questionnaire (SMFQ), which are both validated instruments.ResultsAnalysis was conducted on the first 1047 replies (54% women) from Asia (36%), Africa (40%), Europe (21%) and other (3%). The COVID-19 home confinement had a negative effect on both mental wellbeing and on mood and feelings. Specifically, a significant decrease (pΔ%= 9.4 %) in the total score of mental wellbeing was noted. More individuals (+12.89%) reported a low mental wellbeing “during” compared to “before” home confinement. Furthermore, results from the mood and feelings questionnaire (i.e., depressive symptoms) showed a significant increase by 44.9% (pConclusionThe ECLB-COVID19 survey revealed an increased psychosocial strain triggered by the enforced home confinement. To mitigate this high risk of mental disorders and to foster an Active and Healthy Confinement Lifestyle (AHCL), a crisis-oriented interdisciplinary intervention is urgently needed.

BackgroundAlthough recognised as effective measures to curb the spread of the COVID-19 outbreak, social distancing and self-isolation, have been suggested to generate burden throughout the population. To provide scientific data to help identify risk-factors for the psychosocial strain during the COVID-19 outbreak, an international cross-disciplinary online survey was circulated in April 2020. This report outlines the mental, emotional and behavioural consequences of COVID-19 home confinement.MethodThirty-five research organisations from four continents promoted the survey through their networks to the general society, in Ten different languages. Questions were presented in a differential format with questions related to responses “before” and “during” confinement period.Results1047 replies (54% women) from Western-Asia (36%), North-Africa (40%), Europe (21%) and other countries (3%) were analysed. The COVID-19 home confinement evoked a negative effect on mental wellbeing and emotional status (P < 0.001; 0.43 ≤ d ≤ 0.65) with a greater proportion of individuals experiencing psychosocial and emotional disorders (10% to 16.5%). These psychosocial tolls were associated with unhealthy lifestyle behaviours with a greater proportion of individuals experiencing (i) physical (+15.2%) and social (71.2%) inactivity, (ii) poor sleep quality (12.8%), (iii) unhealthy diet behaviours (10%), and (iv) unemployment (6%). Conversely, participants demonstrated a greater use (15%) of technology solutions during the confinement period.ConclusionThese findings elucidate the risk of psychosocial strain during the current home confinement period and provide a clear remit for the urgent implementation of technology-based intervention to foster an Active and Healthy Confinement Lifestyle (AHCL).

Covid-19 is neither the first nor the last viral epidemic which societies around the world are, were and will be affected by. Which lessons should be taken from the current pandemic situation? The Covid-19 disease is still not well characterised, and many research teams all over the world are working on prediction of the epidemic scenario, protective measures to populations and sub-populations, therapeutic and vaccination issues, amongst others. Contextually, countries with currently low numbers of Covid-19-infected individuals such as Tunisia are intended to take lessons from those countries which already reached the exponential phase of the infection distribution as well as from those which have the exponential phase behind them and record a minor number of new cases such as China. To this end, in Tunisia, the pandemic wave has started with a significant delay compared with Europe, the main economic partner of the country. In this paper, we do analyse the current pandemic situation in this country by studying the infection evolution and considering potential protective strategies to prevent a pandemic scenario. The model is predictive based on a large number of undetected Covid-19 cases that is particularly true for some country regions such as Sfax. Infection distribution and mortality rate analysis demonstrate a highly heterogeneous picture over the country. Qualitative and quantitative comparative analysis leads to a conclusion that the reliable “real-time” monitoring based on the randomised laboratory tests is the optimal predictive strategy to create the most effective evidence-based preventive measures. In contrast, lack of tests may lead to incorrect political decisions causing either unnecessary over-protection of the population that is risky for a long-term economic recession, or under-protection of the population leading to a post-containment pandemic rebound. Recommendations are provided in the context of advanced predictive, preventive and personalised (3P) medical approach.

International audience; Several pieces of research during the last decade in intelligent perception are focused on the development of algorithms allowing vehicles to move efficiently in complex environments. Most of existing approaches suffer from either processing time which do not meet real-time requirements, or inefficient in real complex environment, which also does not meet the full availability constraint of such a critical function. To improve the existing solutions, an algorithm based on curved lane detection by using a Bayesian framework for the estimation of multi-hyperbola parameters is proposed to detect curved lane under challenging conditions. The general idea is to divide a captured image into several parts. The trajectory is modeled by a hyperbola over each part, whose parameters are estimated using the proposed hierarchical Bayesian model. Compared to the existing works in the state of the art, experimental results prove that our approach is more efficient and more precise in road marking detection.

National audience; Diagnosis of sleep disorders is still a challenging issue for a large number of nerve diseases. In this sense, EEG is a powerful tool due to its non-invasive and real-time catacteristics. This modality is being more and more used for diagnosis such as for epilepsy. It is also becoming widely used for many redictive, Preventive and Personalized Medicine (PPPM) applications.To understand sleep disorders, we propose a method to classify EEG signals in order to detect abnormal behaviours that could refect a specificmodification of the sleep pattern. Our method consists of extracting the characteristics based on temporal and spectral analyses with different descriptors. A classifcation is then performed based on these features. Validation on a public available database show promizing results withhigh accuracy levels.

Public health recommendations and governmental measures during the new coronavirus disease (COVID-19) pandemic have enforced numerous restrictions on daily living including social distancing, isolation, and home confinement. While these measures are imperative to mitigate spreading of COVID-19, the impact of these restrictions on psychosocial health is undefined. Therefore, an international online survey was launched in April 2020 to elucidate the behavioral and lifestyle consequences of COVID-19 restrictions. This report presents the preliminary results from more than one thousand responders on social participation and life satisfaction. Methods: Thirty-five research organizations from Europe, North-Africa, Western Asia, and the Americas promoted the survey through their networks to the general society, in 7 languages (English, German, French, Arabic, Spanish, Portuguese, and Slovenian). Questions were presented in a differential format with questions related to responses “before” and “during” confinement conditions. Results: 1047 participations (54% women) from Asia (36%), Africa (40%), Europe (21%), and others (3%) were included in the analysis. Findings revealed psychosocial strain during the enforced COVID-19 home confinement. Large decreases (p &lt, 0.001) in the amount of social activity through family (−58%), friends/neighbors (−44.9%), or entertainment (−46.7%) were triggered by the enforced confinement. These negative effects on social participation were also associated with lower life satisfaction (−30.5%) during the confinement period. Conversely, the social contact score through digital technologies significantly increased (p &lt, 0.001) during the confinement period with more individuals (+24.8%) being socially connected through digital technology. Conclusion: These preliminary findings elucidate the risk of psychosocial strain during the early COVID-19 home confinement period in 2020. Therefore, in order to mitigate the negative psychosocial effects of home confinement, implementation of national strategies focused on promoting social inclusion through a technology-based solution is strongly suggested.

National audience; Hypo-vigilance detection is becoming an important active research areas in the biomedical signal processing field. For this purpose, electroencephalogram (EEG) is one of the most common modalities in drowsiness and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a Convolutional Neural Network (CNN) architecture. We define an experimental protocol using the Emotiv EPOC+ headset. After that, we evaluate our proposed method on a recorded and annotated dataset. The reported results demonstrate high detection accuracy (93%) and indicate that the proposed method is an efficient alternative for hypo-vigilance detection as compared with other methods.

Background: Public health recommendations and governmental measures during the COVID-19 pandemic have resulted in numerous restrictions on daily living including social distancing, isolation and home confinement. While these measures are imperative to abate the spreading of COVID-19, the impact of these restrictions on health behaviours and lifestyles at home is undefined. Therefore, an international online survey was launched in April 2020, in seven languages, to elucidate the behavioural and lifestyle consequences of COVID-19 restrictions. This report presents the results from the first thousand responders on physical activity (PA) and nutrition behaviours. Methods: Following a structured review of the literature, the &ldquo, Effects of home Confinement on multiple Lifestyle Behaviours during the COVID-19 outbreak (ECLB-COVID19)&rdquo, Electronic survey was designed by a steering group of multidisciplinary scientists and academics. The survey was uploaded and shared on the Google online survey platform. Thirty-five research organisations from Europe, North-Africa, Western Asia and the Americas promoted the survey in English, German, French, Arabic, Spanish, Portuguese and Slovenian languages. Questions were presented in a differential format, with questions related to responses &ldquo, before&rdquo, and &ldquo, during&rdquo, confinement conditions. Results: 1047 replies (54% women) from Asia (36%), Africa (40%), Europe (21%) and other (3%) were included in the analysis. The COVID-19 home confinement had a negative effect on all PA intensity levels (vigorous, moderate, walking and overall). Additionally, daily sitting time increased from 5 to 8 h per day. Food consumption and meal patterns (the type of food, eating out of control, snacks between meals, number of main meals) were more unhealthy during confinement, with only alcohol binge drinking decreasing significantly. Conclusion: While isolation is a necessary measure to protect public health, results indicate that it alters physical activity and eating behaviours in a health compromising direction. A more detailed analysis of survey data will allow for a segregation of these responses in different age groups, countries and other subgroups, which will help develop interventions to mitigate the negative lifestyle behaviours that have manifested during the COVID-19 confinement.

Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect lentigo. The proposed method relies on an InceptionV3 architecture combined with data augmentation and transfer learning. The method is validated on RCM data and shows very efficient detection performance with more than 98% of accuracy.

International audience; Parallel imaging technique using several receiver coils provides a fast acquisition of magnetic resonance imaging (MRI) images with high temporal and/or spatial resolutions. Against this background, the most difficult task is the full field of view images reconstruction without noise, distortions and artifacts. In this context, SENSitivity Encoding is considered the most often used parallel MRI (pMRI) reconstruction method in the clinical application. On the one side, solving the inherent reconstruction problems has known significant progress during the last decade. On the other side, the sparse Bayesian regularization for signal/image recovery has generated a great research interest especially when large volumes of data are processed. The purpose of this paper is to develop a novel Bayesian regularization technique for sparse pMRI reconstruction. The new technique is based on a hierarchical Bayesian model using a complex Bernoulli–Laplace mixture in order to promote two sparsity levels for the target image. The inference is conducted using a Markov chain Monte Carlo sampling scheme. Simulation results obtained with both synthetic and real datasets are showing the outperformance of the proposed sparse Bayesian technique compared to other existing regularization techniques.

The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy.

International audience; In the signal processing literature, wavelet transforms have been widely used for compression, restoration or texture processing. In this sense, grouplet transforms have been proposed to account for the geometrical image regularities. A grouplet transform (basis or frame) is based on an a priori fixed association field that groups image coefficients according to geometrical considerations. In this paper, we propose a method for estimating this association field in a Bayesian way. The resulting association field is therefore adaptive to the processed image content. A hierarchical Bayesian model is proposed and the inference is conducted using a Markov Chain Monte Carlo (MCMC) algorithm. The proposed method is tested on standard images in terms of association field quality and quantitative properties of the obtained wavelet coefficients. Specifically, the proposed method provides coefficients with low correlation level, and for which the highest level of energy is concentrated within the 20% most significant. These promising results confirm the potential of the proposed method for several image processing applications such as compression, denoising or restoration.

Internet of Things (IoT) is a hot research topic since several years. IoT has gained a large interest in many application fields such as digital health, smart agriculture or industry. The main focus of the IoT community remains the design of appropriate applications and performant connected objects. In this paper, we address this topic from a signal processing viewpoint. We propose a model to perfom compressed sensing with connected objects where energy and communication constraints araise. The proposed model is formulated in a Bayesian framework and promising results demonstrate its potential in application to EEG signal recording from a connected MindWave device.

Clinical Document Architecture (CDA) (Dolin RH, Alschuler, L, Beebe C, Biron PV, Boyer SL, Essin D, Kimber E, Lincoln T, Mattison JE JAMIA 8:552–569, 2001) is the base standard for the exchange of electronic clinical documents. CDA provides a common architecture, coding, semantic framework, and markup language to support the exchange of structured data component. The main purpose of CDA is to promote interoperability and to help healthcare institutions share data in a meaningful way. However, working with the base standard resulted in different possible implementations and document definitions for the same purpose, which led to interoperability issues. Consolidated Clinical Document Architecture (C-CDA) (https://www.hl7.org/implement/standards/product_brief.cfm?product_id=258. Accessed 20 July 2018), an implementation guide designed for the US realm, aims to solve these problems by reducing the standard’s ambiguity and inconsistencies. This paper presents C-CDA and discusses possible contribution of the Continuity of Care Document (CCD) (INTERNATIONAL, H.L.S.: ‘HL7 Implementation Guide for CDA Release 2:Consolidated CDA Templates for Clinical Notes (US Realm) Volume 2—Templates and Supporting Material’, in Editor (Ed.)^(Eds.): ‘Book HL7 Implementation Guide for CDA Release 2:Consolidated CDA Templates for Clinical Notes (US Realm) Volume 2—Templates and Supporting Material’ (HL7 INTERNATIONAL, 2018, edn.), pp 929) to enhance Tunisian health system interoperability.

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique able to provide the spatial distribu- tion of relevant biochemical compounds commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate metabolite concentrations from in vivo MRSI signals is a crucial require- ment for the clinical utility of this technique. Despite the numerous publications on the topic, accurate quantification is still a challenging problem due to the low signal-to-noise ratio of the data, overlap of spectral lines and the pres- ence of nuisance components. We propose a novel quantification method, which alleviates these limitations by exploiting a spatio-spectral regularization scheme. In contrast to previous methods, the regularization terms are not expressed directly on the parameters being sought, but on appropriate transformed domains. In order to quan- tify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization al- gorithm is proposed. Experiments on synthetic MRSI data demonstrate that the error in the estimated metabolite concentrations is reduced by a mean of 41% with the proposed scheme. Results on in vivo brain MRSI data show the benefit of the proposed approach, which is able to fit overlapping peaks correctly and to capture metabolites that are missed by single-voxel methods due to their lower concentrations.

National audience; A new robust lane marking extraction algorithm for monocular vision is proposed based on Two-Dimension Declivity. It is designed for the urban roads with difficult conditions (shadow, high brightness, etc.). In this paper, we propose a locating system which, from an embedded camera, allows lateral positioning of a vehicle by detecting road markings. The primary contribution of the paper is that it supplies a robust method made up of six steps: (i) Image Pre-processing, (ii) Enhanced Declivity Operator (DE), (iii) Mathematical Morphology, (iv) Labeling, (v) Hough Transform and (vi) Line Segment Clustering. The experimental results have shown the high performance of our algorithm in various road scenes. This validation stage has been done with a sequence of simulated images. Results are very promising: more than 90% of marking lines are extracted for less than 12% of false alarm.

International audience; This paper presents a sparse Bayesian regularization technique for image restoration in parallel magnetic resonance imaging (pMRI). This technique is based on a hierarchical Bayesian model that solves the inverse problem of pMRI reconstruction by promoting sparsity using a Bernoulli-Laplace mixture prior. A Markov Chain Monte Carlo (MCMC) sampling technique is used to numerically approximate the target posterior. Our model allows handling complex-valued data. Promising results obtained on synthetic data demonstrate the performance of the proposed sparse Bayesian restoration model to provide accurate estimation of the target images.

Nowadays fMRI studies are generally conducted over cohorts of individuals in order to study brain activity across different areas and using various stimuli. This paper generalizes a joint parcellation detection estimation (JPDE) model in order to perform an inter and intra subject analysis. The proposed approach allows one to estimate group and subject-level parcellations as well as the corresponding underlying dynamics. Moreover, it allows detecting the evoked activity for each individual. The proposed model is evaluated using synthetic and real fMRI data from a group study.

International audience; Sampling from multi-dimensional and complex distributions is still a challenging issue for the signal processing community. In this research area, Hamiltonian Monte Carlo (HMC) schemes have been proposed several years ago, using the target distribution geometry to perform efficient sampling. More recently, a non-smooth HMC (ns-HMC) scheme has been proposed to generalize HMC for distributions having non-smooth energy functions. This new scheme relies on the use of a proximity operator, which cannot be explicitly calculated for a large class of energy functions. We propose in this paper a fast and more general ns-HMC scheme that can be applied to any energy function by using a Bayesian calculation of the proximity operator, which makes the proposed scheme applicable to any energy function. Moreover, the proposed scheme relies on an interesting property of the proximity operator avoiding heavy calculations at each sampling step. The proposed scheme is tested on different sampling examples involving ℓ p and total variation energy functions.

Automatic object detection is a widely investigated problem in different fields such as military and urban surveillance. The availability of Very High Resolution (VHR) optical remotely sensed data, has motivated the design of new object detection methods that allow recognizing small objects like ships, buildings and vehicles. However, the challenge always remains in increasing the accuracy and speed of these object detection methods. This can be difficult due to the complex background. Therefore, the development of robust and flexible models that analyze remotely sensed data for vehicle detection is needed. We propose in this paper a hierarchical Bayesian model for automatic vehicle detection. Experiments performed using real data indicate the benefit that can be drawn from our approach.

The emergence of efficient algorithms in variational and Bayesian frameworks braught significant advances to the field of inverse problems. However, such problems remain challenging when the observation operator is not perfectly known. In this paper we propose a Bayesian Plug-and-Play (PP) algorithm for solving a wide range of inverse problems where the signal/image is sparse in the original domain and the observation operator has to be estimated. The principle consists of plugging the prior considered for the target observation operator and keep using the same algorithm. The proposed method relies on a generic proximal non-smooth sampling scheme. This genericity makes the proposed algorithm novel in the sense that it can be used to solve a wide range or inverse problems. Our method is illustrated on a deblurring problem with unknown blur operator where promising results are obtained.

Deriving a meaningful functional brain parcellation is a very challenging issue in task-related fMRI analysis. The joint parcellation detection estimation model addresses this issue by inferring the parcels from fMRI data. However, it requires a priori fixing the number of parcels through an initial mask for parcellation. Hence, this difficult task generally depends on the subject. The proposed automatic parcellation approach in this paper overcomes this limitation at the subject-level relying on a Dirichlet process mixture model combined with a hidden Markov random field to estimate the parcels and their number online. The proposed method adopts a variational expectation maximization strategy for inference. Compared to the model selection procedure in the joint parcellation detection estimation framework, our method appears more efficient in terms of computational time and does not require finely tuned initialization. Synthetic data experiments show that our method is able to estimate the right model order and an accurate parcellation. Real data results demonstrate the ability of our method to aggregate parcels with similar hemodynamic behaviour in the right motor and bilateral occipital cortices while its discriminating power is increased compared to its ancestors. Moreover, the obtained HRF estimates are close to the canonical HRF in both cortices. HighlightsA new approach is proposed for hemodynamic brain parcellation in fMRI analysis.It relies on a Dirichlet process mixture model with a hidden Markov random field.It jointly estimates HRFs and parcellation and detects the evoked activity.The number of parcels is inferred online, no need for initial optimum parcellation.Its discrimination power for different HRFs is increased compared to its ancestors.

International audience; Health monitoring is performed on CNES spacecraft using two complementary methods: an utomatic Out-Of-Limits (OOL) checking executed on a set of critical parameters after each new telemetry reception, and a monthly monitoring of statistical features (daily minimum, mean and maximum) of another set of parameters. In this paper we present the limitations of this monitoring system and we introduce an innovative anomaly detection method based on machine-learning algorithms, developed during a collaborative R&D action between CNES and TESA (TElecommunications for Space and Aeronautics). This method has been prototyped and has shown encouraging results regarding its ability to detect actual anomalies that had slipped through the existing monitoring net. An operational-ready software implementing this method, NOSTRADAMUS, has been developed in order to further evaluate the interest of this new type of surveillance, and to consolidate the settings proposed after the R&D action. The lessons learned from the operational assessment of this system for the routine surveillance of CNES spacecraft are also presented in this paper.

Introduction : Postoperative, lingual swelling and necrosis are unusual complications that can lead to life-threatening airway obstruction. We report a case of bilateral ischemic necrosis of the tongue following transoral neurosurgical procedure. Observation : A Davis-Boyle mouth gag was used for tumor exposition and the operation lasted 8 h. At the end of the surgery, we noticed a bluish discoloration with a mild edema of the tongue. The patient had kept intubated in a monitored setting and intravenous steroids were administered. However, the edema increased and necrosis of the tongue appeared 72 h after the surgery. The patient underwent tracheotomy and the necrotic tissue was removed. More than 15 days passed before glossal engorgement resolved. Discussion : The etiology of tongue necrosis is likely related to tissue ischemia, secondary to venous or arterial obstruction. Prolonged use of the Davis-Boyle mouth gag may incur greater risk of ischemic necrosis of the tongue.

International audience; One of the remaining challenges in event-related fMRI is to discriminate between the vascular response and the neural activity in the BOLD signal. This discrimination is done by identifying the hemodynamic territories which differ in their underlying dynamics. In the literature, many approaches have been proposed to estimate these underlying dynamics, which is also known as Hemodynamic Response Function (HRF). However, most of the proposed approaches depend on a prior information regarding the shape of the parcels (territories) and their number. In this paper, we propose a novel approach which relies on the adaptive mean shift algorithm for the parcellation of the brain. A variational inference is used to estimate the unknown variables while the mean shift is embedded within a variational expectation maximization (VEM) framework to allow for estimating the parcellation and the HRF profiles without having any prior information about the number of the parcels or their shape. Results on synthetic data confirms the ability of the proposed approach to estimate accurate HRF estimates and number of parcels. It also manages to discriminate between voxels in different parcels especially at the borders between these parcels. In real data experiment, the proposed approach manages to recover HRF estimates close to the canonical shape in the bilateral occipital cortex.