Alertness States Classification By SOM and LVQ Neural Networks

Several studies have been carried out, using various techniques, including neural networks, to discriminate vigilance states in humans from electroencephalographic (EEG) signals, but we are still far from results satisfactorily useable results. The work presented in this paper aims at improving this status with regards to 2 aspects. Firstly, we introduce an original procedure made of the association of two neural networks, a self organizing map (SOM) and a learning vector quantization (LVQ), that allows to automatically detect artefacted states and to separate the different levels of vigilance which is a major breakthrough in the field of vigilance. Lastly and more importantly, our study has been oriented toward real-worked situation and the resulting model can be easily implemented as a wearable device. It benefits from restricted computational and memory requirements and data access is very limited in time. Furthermore, some ongoing works demonstrate that this work should shortly results in the design and conception of a non invasive electronic wearable devic

Automatic detection of drowsiness in EEG records based on machine learning approaches

editorial reviewe

Séparation des niveaux de vigilance, à partir d'un signal EEG par les cartes auto-organisatrices de Kohonen

Colloque avec actes et comité de lecture. internationale.International audiencePlusieurs études ont déjà été menées pour tenter de discriminer, à l'aide de réseaux de neurones artificiels, les différents états de vigilance d'un sujet humain. Dans ce papier, nous présentons en détail une méthode de séparation des niveaux de vigilance, à partir d'un signal EEG par les cartes auto-organisatrices de Kohonen. Nous y avons associé dès le début des médecins dont l'expertise nous a été précieuse pour le recueil des données et la mise au point de notre modèle

Discrete Wavelet Transform Coefficients for Drowsiness Detection from EEG Signals

peer reviewedThis paper proposes an effective approach to detect drowsiness from EEG signals by using Discrete Wavelet Transform (DWT) coefficients as features. The majority of drowsiness detection systems extract features using FFT to calculate the power spectral density or the DWT to calculate entropy from EEG sub-bands. Although these techniques excel in capturing valuable features in the frequency domain, they omit temporal details essential to the analysis of EEG signals. These details are integrated into coefficients indicating the correlation between the wavelet function and the EEG signal at different times. In our work, we perform a time-frequency analysis of EEG signals using DWT coefficients to preserve this temporal context. Furthermore, the study explores the influence of time segment size on system performance. Subsequently, we determine the most suitable technique to minimize input feature redundancies. Our approach employs just two EEG electrodes, C3 and C4, mirroring common setups for detecting wakefulness and drowsiness. Four classifiers were assessed: decision tree, random forest, multilayer perceptron, and support vector machine. The findings reveal that DWT coefficients enhance drowsiness detection performance, surpassing previous methods

Supervised neuronal approaches for EEG signal classification: experimental studies

Using artificial neural networks for Electroencephalogram (EEG) signal interpretation is a very challenging tasks for several reasons. The first class of reasons refers to the nature of data. Such signals are complex and difficult to process. The second class of reasons refers to the nature of underlying knowledge. Expertise is manifold and difficult to formalize and to be made compatible with a numerical processing. In previous studies we have deeply described that expertise and explained, from theoretical and bibliographical studies, why artificial neural networks could be interesting candidates to perform such a signal interpretation. In this paper, we report recent experiments that we have made on real EEG data in a classification framework. These results are interesting with regard to the state of the art. They also indicate that further work must be done on expertise integration in our neuronal platform

Analyse et classification des états de vigilance par réseaux de neurones

Plusieurs études ont déjà été menées pour tenter de discriminer, à l'aide de réseaux de neurones artificiels, les différents états de vigilance d'un sujet humain. Dans ce rapport, nous rappelons ces études et nous présentons en détail les travaux que nous menons actuellement dans ce même domaine. Notre travail est original sur trois points. Tout d'abord nous avons mené une étude plus large et exhaustive sur les modèles neuronaux utilisés, sur leurs caractéristiques et sur leurs performances. Ensuite, nous y avons associé dès le début des médecins, dont l'expertise nous a été précieuse pour le recueil des données et la mise au point fine de nos modèles. Enfin, et surtout, notre étude a été orientée de manière à pouvoir obtenir un système léger, utilisable sans entrave par un sujet humain. Nous nous sommes en particulier attachés à limiter les besoins de calcul et de mémoire, ainsi que les accès aux données. Cette approche devrait donner lieu prochainement à la réalisation d'un système électronique portable

Pacifica: Poetry International: Revolutions in Tunisian Poetry

A collection of contemporary poetry from Tunisia, including poems in translation with their French and Arabic original texts

Male Oxidative Stress Infertility (MOSI): Proposed Terminology and Clinical Practice Guidelines for Management of Idiopathic Male Infertility

Despite advances in the field of male reproductive health, idiopathic male infertility, in which a man has altered semen characteristics without an identifiable cause and there is no female factor infertility, remains a challenging condition to diagnose and manage. Increasing evidence suggests that oxidative stress (OS) plays an independent role in the etiology of male infertility, with 30% to 80% of infertile men having elevated seminal reactive oxygen species levels. OS can negatively affect fertility via a number of pathways, including interference with capacitation and possible damage to sperm membrane and DNA, which may impair the sperm’s potential to fertilize an egg and develop into a healthy embryo. Adequate evaluation of male reproductive potential should therefore include an assessment of sperm OS. We propose the term Male Oxidative Stress Infertility, or MOSI, as a novel descriptor for infertile men with abnormal semen characteristics and OS, including many patients who were previously classified as having idiopathic male infertility. Oxidation-reduction potential (ORP) can be a useful clinical biomarker for the classification of MOSI, as it takes into account the levels of both oxidants and reductants (antioxidants). Current treatment protocols for OS, including the use of antioxidants, are not evidence-based and have the potential for complications and increased healthcare-related expenditures. Utilizing an easy, reproducible, and cost-effective test to measure ORP may provide a more targeted, reliable approach for administering antioxidant therapy while minimizing the risk of antioxidant overdose. With the increasing awareness and understanding of MOSI as a distinct male infertility diagnosis, future research endeavors can facilitate the development of evidence-based treatments that target its underlying cause