Neuronal Spectral Analysis of EEG and Expert Knowledge Integration for Automatic Classification of Sleep Stages

Being able to analyze and interpret signal coming from electroencephalogram (EEG) recording can be of high interest for many applications including medical diagnosis and Brain-Computer Interfaces. Indeed, human experts are today able to extract from this signal many hints related to physiological as well as cognitive states of the recorded subject and it would be very interesting to perform such task automatically but today no completely automatic system exists. In previous studies, we have compared human expertise and automatic processing tools, including artificial neural networks (ANN), to better understand the competences of each and determine which are the difficult aspects to integrate in a fully automatic system. In this paper, we bring more elements to that study in reporting the main results of a practical experiment which was carried out in an hospital for sleep pathology study. An EEG recording was studied and labeled by a human expert and an ANN. We describe here the characteristics of the experiment, both human and neuronal procedure of analysis, compare their performances and point out the main limitations which arise from this study

Implementation of a low‐power LVQ architecture on FPGA

This study presents an architecture‐optimising methodology for embedding an learning vector quantization (LVQ) neural network on an field programmable gate array (FPGA) device. The embedded architecture contains both learning and decision circuitry and is optimised towards the lowest power/energy consumption. The low‐power/energy architecture is obtained through the selection of the best one amongst a number of architectures produced by FPGA software design tools that combine power, area and the ergonomic utilisation of internal FPGA resources. A complete characterisation of power at the architectural level was carried out using the Xpower tool. An analytical power model was determined by the following parameters: area, delay and LVQ topology. Concerning the authors’ architecture, there is a 28% gain in the area design. Moreover, it consumes 8% power in the nanoboard 3000 compared with the other ones

Architecture dédiée pour le traitement temps réel des signaux physiologiques : spécification algorithmique et méthodologie d'implantation sur circuits reconfigurables

Nous traitons dans cet article l'optimisation de l'implantation sur circuit FPGA d'un réseau de neurones à partir d'une spécification algorithmique sous la forme d'un Graphe Factorisé et Conditionné de Dépendances de Données (GFCDD). Nous proposons une approche d'optimisation automatique de l'implantation d'un réseau de neurone LVQ (Learning Vector Quantification) en exploitant au mieux l'aspect motif répétitif dans l'algorithme neuronal par une méthodologie « Adéquation Algorithme Architecture ». Nous avons pu générer une implantation de ce réseau de neurone LVQ pour des couches de sortie de dimension variable en minimisant le temps de conception.. Dans la phase d'optimisation, nous avons minimisé la consommation des ressources matérielles tout en respectant les contraintes temporelles liées au contexte de l'application exemple choisie: la décision en temps réel sur l'état de vigilance par traitement des signaux physiologiques

End-to-End Mobile System for Diabetic Retinopathy Screening Based on Lightweight Deep Neural Network

Diabetic Retinopathy (DR) is the leading cause of visual impairment among working-aged adults. Screening and early diagnosis of DR is essential to avoid visual acuity reduction and blindness. However, a worldwide limited access to ophthalmologists may prevent an early diagnosis of this blinding condition. In this paper, we propose a novel method for screening DR from smartphone-captured fundus images. The main challenges are to perform higher accurate detection even with reduced quality of handheld captured fundus images and to provide the result into the smartphone used for acquisition. For such a need, we apply transfer learning to the lightweight deep neural network "NasnetMobile" which is used as a feature descriptor, while configuring a multilayer perceptron classifier to deduce the DR disease, in order to take benefit from their lower complexity. A dataset composed of 440 fundus images is structured, where the acquisition and statement are performed by expert ophthalmologists. A cross-validation process is conducted where 95.91% accuracy, 94.44% sensitivity, 96.92% specificity and 95.71% precision in average are achieved. In addition, the whole processing flowchart is implemented into a mobile device, where the execution time is under one second whatever the fundus image is. Those performances allow deploying the proposed system in a clinical context

Mobile‐aided screening system for proliferative diabetic retinopathy

International audienceNeoVascularization (NV) occurs in the Proliferative Diabetic Retinopathy (PDR) stage, where the development progress of new vessels presents a high risk for severe vision loss and blindness. Therefore, early NV detection is primordial to preserve patient's vision. Several automated methods have been proposed to detect the NV on retinograph-captured fundus images. However, their employment is constrained by the reduced ophthalmologist per person ratio and the expensive equipment required for image capturing. This paper presents a novel method for NV detection in smartphone-captured fundus images. The implementation of the method on a smartphone device having an optical lens for fundus capturing leads to a Mobiles-Aided-Screening system of PDR (MAS-PDR). The challenge is to ensure accurate and robust detection even with the moderate quality of fundus image, with a reduced execution time. Within this objective, we identify the major criteria of neovascularized vessels which are tortuosity, width, bifurcation and density. Our main contribution consists in proposing a sharp feature to reflect each criterion on reduced computational complexity processing. Therefore, the feature set is provided to a random forest classifier in order to deduce whether the fundus image is in the PDR stage. A dataset raised from publicly databases is used on a 10-cross validation process where average accuracy of 98.69%, sensitivity of 97.73% and a specificity of 99.12% are achieved. To evaluate the method robustness, the same experimentation is repeated after applying motion blur filters to the fundus image dataset, where 98.91% accuracy, 96.75% sensitivity, and 100% specificity are deduced. Moreover, NV screening is performed under three seconds when executed in smartphone devices demonstrating the appropriateness of our suggested method to MAS-PDR

Etude algorithmique et conception d'architectures dédiées à la reconstruction 3D d'images médicales

electronic version (8 pp.

Grammar Formalism for ECG Signal Interpretation and Classification

This paper shows that the grammar formalism can be pushed to be applied for the description and the classification of electrocardiograms signals (ECG). We will describe an ECG signal as a sequence of tokens based on specific vocabularies and a set of grammatical rules. QRS complexes, RR distances, PR and QT intervals will be calculated. This type of work is intended for medical diagnosis assistance of an ECG signal