Hadj Batatia

• Msc, MEng, PhD, HDR
• Professor (Associate) at Heriot-Watt University

Despite tremendous efforts devoted to the area, image texture analysis is still an open research field. This paper presents an algorithm and experimental results demonstrating the feasibility of developing automated tools to detect abnormal X-ray images based on tissue attenuation. Specifically, this work proposes using the variability characterise...

La somnolence des conducteurs est une cause majeure d'accidents de la route. L'électroencéphalogramme (EEG) est considéré comme le prédicteur le plus robuste de cet état cérébral. Cet article propose une nouvelle méthode de détection de somnolence à l'aide d'une seule électrode, avec un potentiel d'implémentation temps réel. L'article présente d'ab...

Network intrusion detection systems (NIDS) play a vital role in defending against cybersecurity threats. One effective way of detecting attacks is to analyse their footprint on the network traffic logs. Flow-based logging is a standard method for logging network traffic. Given the high volume of traffic, it is unpractical to manually analyse it. Th...

Artificial neural networks (ANNs) are today the most popular machine learning algorithms. ANNs are widely applied in various fields such as medical imaging and remote sensing. One of the main challenges related to the use of ANNs is the inherent optimization problem to be solved during the training phase. This optimization step is generally perform...

Electroencephalography (EEG) is an effective tool for neurological disorders diagnosis such as seizures, chronic fatigue, sleep disorders, and behavioral abnormalities. Various artifacts types may impact EEG signals regardless the used, resulting in an erroneous diagnosis. Various data analysis tools have therefore been developed in the biomedical...

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 ad...

Chapter 11 demonstrates the potential of artefacts detection approach in electro- encephalography, using the Hampel filter to correct different types of artefacts. Also, a complete state-of-the-art is introduced along with a recommended bibliography to research these topics.

Prediction methods of glioblastoma tumours growth constitute a hard task due to the lack of medical data, which is mostly related to the patients' privacy, the cost of collecting a large medical data set, and the availability of related notations by experts.In this study, the authors propose a synthetic medical image generator (SMIG) with the purpo...

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 Go...

Background Public health recommendations and government measures during the COVID-19 pandemic have enforced restrictions on daily-living. 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 (ECLB-COVI...

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational...

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) signals is a key signal processing problem. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new SWD method with a low computational complexity that can be easily t...

This paper deals with the detection of mu-suppression from electroencephalographic (EEG) signals in brain-computer interface (BCI). For this purpose, an efficient algorithm is proposed based on a statistical model and a linear classifier. Precisely, the generalized extreme value distribution (GEV) is proposed to represent the power spectrum density...

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 healt...

Although recognised as effective measures to curb the spread of the COVID-19 outbreak, social distancing and self-isolation have been suggested to generate a 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...

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 Incepti...

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 launche...

The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals. In this paper, we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram (EEG) signals. The underlying idea was to design...

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 lifest...

This paper deals with the detection of mu-suppression from electroencephalographic (EEG) signals in brain-computer interface (BCI). For this purpose, an efficient algorithm is proposed based on a statistical model and a linear classifier. Precisely, the generalized extreme value distribution (GEV) is proposed to represent the power spectrum density...

Background Public 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....

Background: Although 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-discipli...

Background: Public 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 emoti...

Background Public 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 a...

The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals. In this paper, we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter EEG signals. The underlying idea is to design an EEG filter that enha...

Spike-and-wave discharge (SWD) pattern classification in electroencephalography (EEG) signals is a key problem in signal processing. It is particularly important to develop a SWD automatic detection method in long-term EEG recordings since the task of marking the patters manually is time consuming, difficult and error-prone. This paper presents a n...

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 f...

This paper proposes a new algorithm for epileptic seizure onset detection in EEG signals. The algorithm relies on the measure of the entropy of observed data sequences. Precisely, the data is decomposed into different brain rhythms using wavelet multi-scale transformation. The resulting coefficients are represented using their generalized Gaussian...

This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm...

Airports today play an important economic role. As intermodal transport terminals, they have to channel important flows of passengers and goods. In recent decades, they have been the target of terrorist attacks and the place where all types of traffic pass. Consequently, they are faced with the dual objective of maximization of security system as w...

Detecting skin lentigo in reflectance confocal microscopy images is an important and challenging problem. This imaging modality has not yet been widely investigated for this problem and there are a few automatic processing techniques. They are mostly based on machine learning approaches and rely on numerous classical image features that lead to hig...

This paper deals with the characterization and classification of reflectance confocal microscopy images of human skin. The aim is to identify and characterize the lentigo, a phenomenon that originates at the dermo-epidermic junction of the skin. High resolution confocal images are acquired at different skin depths and are analyzed for each depth. H...

This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering from epilepsy. Precisely, EEG signals are transformed i...

This paper studies a new Bayesian algorithm for the joint reconstruction and classification of reflectance confocal microscopy (RCM) images, with application to the identification of human skin lentigo. The proposed Bayesian approach takes advantage of the distribution of the multiplicative speckle noise affecting the true reflectivity of these ima...

A reliable leadfield matrix is needed to solve the M/EEG source localization problem. The computation of this matrix requires several physical parameters, including the conductivity of the tissues that compose the subject’s head. Since it is not precisely known, we modify a recent Bayesian algorithm to estimate the skull conductivity jointly with t...

Efficient sampling from high-dimensional distributions is a challenging issue that is encountered in many large data recovery problems. In this context, sampling using Hamiltonian dynamics is one of the recent techniques that have been proposed to exploit the target distribution geometry. Such schemes have clearly been shown to be efficient for mul...

M/EEG source localization is an open research issue. To solve it, it is important to have good knowledge of several physical parameters to build a reliable head operator. Amongst them, the value of the conductivity of the human skull has remained controversial. This report introduces a novel hierarchical Bayesian framework to estimate the skull con...

This paper deals with EEG source localization. The aim is to perform spatially coherent focal localization and recover temporal EEG waveforms, which can be useful in certain clinical applications. A new hierarchical Bayesian model is proposed with a multivariate Bernoulli Laplacian structured sparsity prior for brain activity. This distribution app...

The classification of epileptic seizure events in EEG signals is an important problem in biomedical engineering. In this paper we propose a Bayesian classification method for multivariate EEG signals. The method is based on a multilevel 2D wavelet decomposition that captures the distribution of energy across the different brain rhythms and regions,...

This paper addresses the problem of designing efficient sampling moves in order to accelerate the convergence of MCMC methods. The Partially collapsed Gibbs sampler (PCGS) takes advantage of variable reordering, marginalization and trimming to accelerate the convergence of the traditional Gibbs sampler. This work studies two specific moves which al...

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique able to provide the spatial distribution 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 di...

In high-grade gliomas, the tumor boundaries and the degree of infiltration are difficult to define due to their heterogeneous composition and diffuse growth pattern. Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive technique able to provide information on brain tumor biology not available from conventional anatomical imaging. In th...

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biome...

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique able to provide the spatial distribution 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 di...

In this paper, we propose a hierarchical Bayesian model approximating the l20 mixed-norm regularization by a multivariate Bernoulli Laplace prior to solve the EEG inverse problem by promoting spatial structured sparsity. The posterior distribution of this model is too complex to derive closed-form expressions of the standard Bayesian estimators. An...

This report introduces a new hierarchical Bayesian model for the EEG source localization problem. This model promotes structured sparsity to search for focal brain activity. This sparsity is obtained via a multivariate Bernoulli Laplacian prior assigned to the brain activity approximating an $\ell_{20}$ pseudo norm regularization in a Bayesian fram...

Source localization in electroencephalography has received an increasing amount of interest in the last decade. Solving the underlying ill-posed inverse problem usually requires choosing an appropriate regularization. The usual ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm has been con...

The paper presents an unsupervised algorithm for the joint segmentation of 4-D PET-CT images. The proposed method is based on a bivariate-Poisson mixture model to represent the bimodal data. A Bayesian framework is developed to label the voxels as well as jointly estimate the parameters of the mixture model. A generalized four-dimensional Potts-Mar...

Extracting information from scalp EEG signals is a challenging biomedical signal processing problem that has a potentially strong impact in the diagnosis and treatment of numerous neurological conditions. In this work we study a new methodology for extracting information from EEG signals from patients suffering from epilepsy. The methodology is bas...

Purpose Respiratory motion is a source of artifacts that reduce image quality in PET. Four dimensional (4D) PET/CT is one approach to overcome this problem. Existing techniques to limiting the effects of respiratory motions are based on prospective phase binning which requires a long acquisition duration (15–25 min). This time is uncomfortable for...

MR spectroscopic imaging (MRSI) can characterize biochemical, metabolic and pathological changes in brain tissues before they are visible from conventional anatomical images. This makes MRSI a powerful tool to define biologically tailored target volumes for dose escalation in radiation therapy treatments. Although the numerous existing quantificati...

Background Early diagnosis and rapid surgical excision are essential for improving the prognosis of melanoma patients. Reflectance confocal microscopy has been validated as a feasible procedure for in vivo diagnosis of melanoma but cannot be used to measure tumour thickness. However, ultrasonography and optical coherence tomography may allow melano...

Sparse signal/image recovery is a challenging topic that has captured a great interest during the last decades. To address the ill-posedness of the related inverse problem, regularization is often essential by using appropriate priors that promote the sparsity of the target signal/image. In this context, ℓ0 + ℓ1 regularization has been widely inves...

Sparse signal/image recovery is a challenging topic that has captured a great interest during the last decades, especially in the biomedical field. Many techniques generally try to regularize the considered ill-posed inverse problem by defining appropriate priors for the target signal/image. The target regularization problem can then be solved eith...

Sparse signal recovery has been widely investigated during the last decade. This issue arises especially in large data applications where regularization is essential to recover a stable solution of the related inverse problem. Bayesian and variational regularization methods rely on different assumptions and lead to different estimators. Although va...

In this paper we seek to exploit information geometry in order to define the Riemannian metric of the manifold associated with nonparametric active contour models from the exponential family. This Riemannian metric is obtained through a relationship between the contour's energy functional and the likelihood of the categorical latent variables of a...

In this paper we seek to exploit information geometry in order to define the Riemannian structure of the statistical manifold associated with the Chan-Vese active contour model. This Riemannian structure is obtained through a relationship between the contour's Mumford-Shah energy functional and the likelihood of the categorical latent variables of...

Magnetic resonance spectroscopy imaging (MRSI) is a powerful non-invasive tool for characterising markers of biological processes. This technique extends conventional MRI by providing an additional dimension of spectral information describing the abnormal presence or concentration of metabolites of interest. Unfortunately, in vivo MRSI suffers from...

Patient-specific dosimetry in nuclear medicine relies on activity quantification in volumes of interest from scintigraphic imaging. Clinical dosimetry protocols have to be benchmarked against results computed from test phantoms. The design of an adequate model is a crucial step for the validation of image-based activ­ ity quantification. We propose...

Purpose: Respiratory motion creates artifacts in positon emission tomography with computed tomography (PET/CT) images especially for lung tumors, and can alter diagnosis. To account for motion effects, respiratory gating techniques have been developed. However, the lack of measures strongly correlated with tumor motion limits their accuracy. The a...

This paper addresses the problem of estimating the Potts parameter jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on requires computing the intractable normalizing constant of the Potts model. In the prop...

Sparse regularization has been receiving an increasing interest in the literature. Two main difficulties are encountered when performing sparse regularization. The first one is how to fix the parameters involved in the regularization algorithm. The second one is to optimize the inherent cost function that is generally non differentiable, and may al...

This study is dedicated to the problem of non linear attitude control for a rigid large angle maneuver satellite. The non linear control method is based upon the linearization of the input-output of the system, also called feedback linearization, via state feedback. The output function is chosen amongst the attitude quaternion parameters.

This paper deals with the restoration of Positron Emission Tomography images. The partial volume effect creates blurring in such images and causes inaccurate quantization. This artefact is due to the complex geometry of the acquisition system. We propose to represent this complexity by a spatially variable point spread function. The PSF is first me...

This study assesses the accuracy of prospective phase-gated PET/CT data binning and presents a retrospective data binning method that improves image quality and consistency. Respiratory signals from 17 patients who underwent 4D PET/CT were analysed to evaluate the reproducibility of temporal triggers used for the standard phase-based gating method....

Radiation dose evaluation in Molecular Radiotherapy requires the localization of injected radioactive sources by scintigraphic imaging, both in space (within the patient) and time (pharmacokinetics). Besides, absorbed dose calculation must be carried out with regards to propagating media, emitted radiations and their interactions within the patient...

This report considers the problem of computing the Cramer-Rao bound for the parameters of a Markov random field. Computation of the exact bound is not feasible for most fields of interest because their likelihoods are intractable and have intractable derivatives. We show here how it is possible to formulate the computation of the bound as a statist...

Biological tissue characterization requires adequate models of the data. Research in ultrasound image modeling has mainly focused on statistical methods. Some authors have addressed the fractal properties of such images using fractional Brownian motion model. However, recent studies have shown that skin ultrasound signals have multifractal properti...

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is...

Résumé Cet article présente un algorithme Bayésien pour la seg-mentation d'images de Tomographie par Emission de Po-sitons (TEP). Tenant compte des phénomènes physiques sous-jacents à la formation de l'image TEP, nous mo-délisons l'activité des tissus comme un mélange de dis-tributions Poisson-Gamma. Un algorithme Bayésien hié-rarchique de type Mon...

Résumé Cet article traite de la reconstruction 4D d'images de tomodensitométrie. Les techniques TDM-4D existantes souffrent de l'effet du mouvement respiratoire qui altère la localisation des organes et l'activité des tumeurs. Étant donné une séquence de coupes acquises à différentes po-sitions respiratoires, la méthode proposée permet d'inter-pole...

Starting from the widely accepted point-scattering model, this paper establishes, through analytical developments, that ultrasound signals backscattered from skin tissues converge to a complex Levy flight random process with non- Gaussian α-stable statistics. The envelope signal follows a generalized (heavy-tailed) Rayleigh distribution. It is show...

The concept of tacit knowledge has received a great deal of attention recently. From a knowledge creation point of view, much of the related discussion fails to provide any deep insights. Notwithstanding the superficial treatments commonly encountered, the concept embodies crucial aspects of learning that are critical for the success of KP-Lab. Ind...

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is...

This paper presents a Bayesian algorithm for PET image segmentation. The proposed method, which is derived from PET physics, models tissue activity using a mixture of Poisson-Gamma distributions. Moreover, a Markov field is proposed to model the spatial correlation between mixture components. Then, segmentation is performed using an Markov chain Mo...