Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data

Magnetoencephalography (MEG) recordings of patients with epilepsy exhibit spikes, a typical biomarker of the pathology. Detecting those spikes allows accurate localization of brain regions triggering seizures. Spike detection is often performed manually. However, it is a burdensome and error prone task due to the complexity of MEG data. To address this problem, we propose a 1D temporal convolutional neural network (Time CNN) coupled with a graph convolutional network (GCN) to classify short time frames of MEG recording as containing a spike or not. Compared to other recent approaches, our models have fewer parameters to train and we propose to use a GCN to account for MEG sensors spatial relationships. Our models produce clinically relevant results and outperform deep learning-based state-of-the-art methods reaching a classification f1-score of 76.7% on a balanced dataset and of 25.5% on a realistic, highly imbalanced dataset, for the spike class.Comment: This work has been submitted to IEEE ISBI 2024 for possible publicatio

ThingsMigrate: Platform-Independent Migration of Stateful JavaScript IoT Applications

The Internet of Things (IoT) has gained wide popularity both in academic and industrial contexts. As IoT devices become increasingly powerful, they can run more and more complex applications written in higher-level languages, such as JavaScript. However, by their nature, IoT devices are subject to resource constraints, which require applications to be dynamically migrated between devices (and the cloud). Further, IoT applications are also becoming more stateful, and hence we need to save their state during migration transparently to the programmer. In this paper, we present ThingsMigrate, a middleware providing VM-independent migration of stateful JavaScript applications across IoT devices. ThingsMigrate captures and reconstructs the internal JavaScript program state by instrumenting application code before run time, without modifying the underlying Virtual Machine (VM), thus providing platform and VM-independence. We evaluated ThingsMigrate against standard benchmarks, and over two IoT platforms and a cloud-like environment. We show that it can successfully migrate even highly CPU-intensive applications, with acceptable overheads (about 30%), and supports multiple migrations

High electrode activity of nanostructured, columnar ceria films for solid oxide fuel cells

Highly porous oxide structures are of significant importance for a wide variety of applications in fuel cells, chemical sensors, and catalysis, due to their high surface-to-volume ratio, gas permeability, and possible unique chemical or catalytic properties. Here we fabricated and characterized Sm_(0.2)Ce_(0.8)O_(1.9−δ) films with highly porous and vertically oriented morphology as a high performance solid oxide fuel cell anode as well as a model system for exploring the impact of electrode architecture on the electrochemical reaction impedance for hydrogen oxidation. Films are grown on single crystal YSZ substrates by means of pulsed laser deposition. Resulting structures are examined by SEM and BET, and are robust up to post-deposition processing temperatures as high as 900 °C. Electrochemical properties are investigated by impedance spectroscopy under H_2–H_2O–Ar atmospheres in the temperature regime 450–650 °C. Quantitative connections between architecture and reaction impedance and the role of ceria nanostructuring for achieving enhanced electrode activity are presented. At 650 °C, _pH_2O = 0.02 atm, and _pH_2 = 0.98 atm, the interfacial reaction resistance attains an unprecedented value of 0.21 to 0.23 Ω cm^2 for porous films 4.40 μm in thickness

Pertinence du neuromonitoring multimodal dans les lesions doubles : A propos de la prise en charge, a l’hopital neurologique de lyon, d’une scoliose congenitale avec deficit neurologique rapide

Nous rapportons un cas illustrant qu’une évaluation clinique, radiologique et neurophysiologique exhaustive est obligatoire avant une chirurgie de scoliose sévère. Une patiente a été référée pour bénéficier d’une correction chirurgicale d’une scoliose congénitale malformative. L’examen clinique à l’admission a révélé une tétraparésie qui a imposé un changement urgent de stratégie chirurgicale. L’imagerie par résonance magnétique a objectivé un neurofibrome C2-C3. Les potentiels évoqués somesthésiques (PES) des membres supérieurs et inférieurs étaient normaux mais les potentiels évoqués moteurs (PEM) étaient abolis. Leur réapparition peropératoire lors de la résection du neurofibrome a précédé une amélioration clinique progressive. La récupération spectaculaire des PEM a permis une correction de la déformation de la colonne vertébrale sous surveillance peropératoire plusieurs mois plus tard.   French title: Relevance of multimodal neuromonitoring in dual lesions: About the management, in lyon neurological hospital, of congenital scoliosis with rapid neurological deficit We report a case illustrating that exhaustive clinical, radiological and neurophysiological assessment is mandatory before severe scoliosis surgery. A patient was referred for surgical correction of congenital malformative scoliosis. Clinical examination admission revealed a tetraparesis that enforced an urgent change in surgical strategy. Magnetic Resonance Imaging disclosed a C2-C3 neurofibroma. Upper and lower limbs somatosensory evoked potentials were normal but motor evoked potentials (MEPs) were abolished. Their intra-operative reappearance at the time of neurofibroma resection preceded a progressive clinical improvement. The spectacular MEPs recovery allowed correction of the spinal deformity under intraoperative monitoring several months later

Laser excited super resolution thermal imaging for nondestructive inspection of internal defects

A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques.Comment: 9 pages, 6 figure

Exploring the Electrophysiological Correlates of the Default-Mode Network with Intracerebral EEG

While functional imaging studies allow for a precise spatial characterization of resting state networks, their neural correlates and thereby their fine-scale temporal dynamics remain elusive. A full understanding of the mechanisms at play requires input from electrophysiological studies. Here, we discuss human and non-human primate electrophysiological data that explore the neural correlates of the default-mode network. Beyond the promising findings obtained with non-invasive approaches, emerging evidence suggests that invasive recordings in humans will be crucial in order to elucidate the neural correlates of the brain's default-mode function. In particular, we contend that stereotactic-electroencephalography, which consists of implanting multiple depth electrodes for pre-surgical evaluation in drug-resistant epilepsy, is particularly suited for this endeavor. We support this view by providing rare data from depth recordings in human posterior cingulate cortex and medial prefrontal cortex that show transient neural deactivation during task-engagement

Extraction of functional dynamic networks describing patient's epileptic seizures

International audienceIntracranial EEG studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone towards remote brain areas. This appears like patient-specific time-varying networks that has to be extracted and characterised. Functional Connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time-consuming and requires high expertise. In the present study [1], we propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterise the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes. The method provides a number of pathological FC subgraphs with their temporal course of activation. The method can be applied to several seizures of the patient to extract reproducible subgraphs. To validate the extraction, we compare the activated subgraphs obtained by BTND to interpretation of SEEG signals recorded in 27 seizures from 9 different patients. We a found a good agreement about the activated subgraphs and the corresponding brain regions involved during the seizures and their activation dynamics

Réduction de dimension tensorielle parcimonieuse: Application au clustering de connectivité fonctionnelle

National audiencek-means est un algorithme célèbre pour le clustering de données, mais ses performances se dégradent sur des données de grandes dimensions. Nous proposons des décompositions tensorielles parcimonieuses pour réduire la dimension des données avant d'appliquer k-means. Nous illustrons notre méthode sur des mesures de connectivité fonctionnelle d'EEG de crises épileptiques. Abstract-k-means is famous to cluster a dataset, however it is known to perform badly on high dimensional data. To apply it on EEG functional connectivity measures, as function of the time and for different seizures of a same patient, we develop a new sparse tensorial decomposition to reduce the dimensions of the data before applying k-means