Volatile Constituents And Antimicrobial Activity Of Lavandula Stoechas L. Oil From Tunisia

International audienceAn oil obtained from the dried leaves of Lavandula stoechas L. in 0.77% yield was analyzed by capillary GC and GUMS. Fenchone (68.2%) and camphor (11.2%) were the main components of the 28 identified molecules. This oil has been tested for antimicrobial activity against six bacteria, and two fungi. The results showed that this oil was active against all of the tested strains; Staphylococcus aureus was the more sensitive strain

Multi-Classification of epileptic High Frequency Oscillations using a Time-Frequency image-based CNN

International audienceHigh Frequency Oscillations (HFOs) in intracranial ElectroEncephaloGraphic (iEEG) signals are considered as promising biomarkers for localizing the epileptogenic zone. Visual marking of these particular activities is the typical way not only for the identification of HFOs but also for their discrimination from other transient events such as Interictal Epileptic Spikes (IESs). However, this remains a highly time-consuming process. To cope with this issue, several approaches have been already proposed for an automatic detection of HFOs. Most of these approaches are based on machine learning algorithms where relevant features are to be extracted for efficient classification. Looking for these relevant features is however a challenging task and can be avoided by resorting to deep learning. In this paper, a new method for HFOs multi-classification based on a convolutional neural network (CNN) is proposed. The proposed CNN model is based on Time-Frequency representation of HFOs computed using Stockwell transform. The efficiency of the proposed method is confirmed using real iEEG signals and compared with a supervised machine learning approach based on support vector machine (SVM) as classifier. © 2022 IEEE

Interactive interface for spatio-temporal mapping of epileptic human brain using characteristics of high frequency oscillations (HFOs)

International audienceSpontaneous High Frequency Oscillations (HFOs) have been considered as emerging specific biomarkers of the epileptogenic region. As a first issue, a significant difference in the implementation of automatic HFOs detection methods can sometimes occur between researchers. In addition, clinicians are not even particularly familiar with the concept of signal and image processing, and programming skills. To overcome these limitations, we propose a plug-and-play interactive Graphical User Interface (GUI) that incorporates an amalgamation of six validated methods used for detecting and quantifying of HFOs events. As a second issue, the most automated HFOs detection methods to date have a high false detection rate and low specificity, ranging, in some cases up to 80% and below 37% respectively. Therefore, the eventual utilization of HFOs detection algorithms in clinical settings requires a checking step to save clinically relevant HFOs and remove spurious oscillations from the detection results. As a last issue addressed in the present study, the major previous HFOs studies have been limited only to the detection and classification of HFOs, but only a few studies have been conducted to efficiently follow the neural dynamics of epileptic focus by studying HFOs characteristics through different brain regions and clinical stages. Therefore, in our software, the brain mapping of HFOs characteristics is done based on the duration, the inter-duration, the average frequency, and the power of HFOs. The present developed software may be considered helpful for understanding the functional significance of HFOs and also to reduce the interaction gap between fundamental research and applied clinical practice related to HFOs. © 2023 Elsevier Lt

Comparison of granger causality measures to detect effective connectivity in the context of epilepsy

International audienceEffective connectivity can be modeled and quantified with a number of techniques. The aim of this study is to reveal the direction of the information flow and to quantify the magnitude of coupling between epileptic brain structures using Granger Causality (GC) approaches. Since traditional linear GC cannot identify non-linear effects in the data, the non-linear extension of this measure is recommended. A comparative study between linear and non-linear GC is performed to determine the importance of the non-linear measure in the study of complex dynamical systems as neural networks. Experiments are first conducted on a linear autoregressive model, then on a non-linear model and finally on a model of intracranial EEG signals generation before giving some conclusions on the relevance on the different indices. © 2017 IEEE

Investigation of nonlinear granger causality in the context of epilepsy

International audienceGranger causality approaches have been widely used to estimate effective connectivity in complex dynamic systems. These techniques are based on the building of predictive models which not only depend on a proper selection of the predictive vectors size but also on the chosen class of regression functions. The question addressed in this paper is the estimation of the model order in the computation of Granger causality indices to characterize the propagation flow between simulated epileptic signals. In this contribution, a new strategy is proposed to select a suitable model order for potentially nonlinear systems. A nonlinear vectorial autoregressive model based on a wavelet network is considered for the identification and an optimal nonlinear model order is selected using the Bayesian information criterion and imported in nonlinear kernel predictors to derive Granger causality. Simulations are firstly conducted on a linear autoregressive model, then on toy nonlinear models and, finally, on simulated intracranial electroencephalographic signals obtained from an electrophysiology based model to reveal the directional relationships between time series data. The performance of our approach proves the effectiveness of the new strategy in the Granger index estimation. © EURASIP 2017

Pitfalls of spikes filtering for detecting High Frequency Oscillations (HFOs)

International audienceCerebral High Frequency Oscillations (HFOs) have recently been discovered in epileptic EEG recordings. HFOs have been defined as spontaneous rhythmic oscillations with short duration, operating approximately in the frequency range between 80 Hz and 500Hz. HFOs have been considered as reliable and precise biomarkers for delineating the epileptogenic tissue. Also, HFOs have a profound impact for understanding the cerebral mechanisms involved in the generation of epileptic seizures. Therefore, several algorithms for HFOs detection with different performance and computational complexity have been proposed over the last few years. One of the major issues associated with HFOs detection algorithms applied on filtered EEG signals is how to differentiate spurious oscillations from true HFOs. The objective of this study is to highlight the original phenomena of spurious oscillations resulting from the filtering of simulated spikes. Our results are then validated on real spikes. In our study, three filtering methods are considered: The Finite Impulse Response (FIR), the Complex Morlet Wavelet (CMOR) and the Matching Pursuit based technique (MP). © 2021 IEEE

Detection of Epileptic High Frequency Oscillations Using Support Vector Machines

International audienceRecently, several studies have proved that High Frequency Oscillations (HFOs) of [80500] Hz are reliable biomarkers for delineating the epileptogenic zone. The total duration of HFOs is extremely short compared to the entire duration of EEG dataset to be analyzed. Therefore, visual marking of HFOs is timeconsuming and laborious process. In order to promote the clinical use of HFOs oscillations as reliable biomarkers of epileptogenic tissue and to conduct large-scale investigations on cerebral HFOs activities, several automatic detection techniques have been proposed over the past few years. In the present framework, we propose a novel approach for detecting HFOs based on Support Vector Machines (SVM). Our method is subsequently compared with six other methods. HFOs detection performance is evaluated in terms of sensitivity, false discovery rate, area under the ROC curve and execution time. Our results demonstrate that SVM approach yields low false detection (FDR = 6.36%) but, in its current implementation, is moderately sensitive to detect HFOs with a sensitivity of 71.06%. © 2020 IEEE