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

Epileptic seizure detection using multivariate empirical mode decomposition and support vector machines

International audienceAutomatic detection of epileptic seizures is a very crucial step for diagnosing patients with drug-resistant epilepsies. If visual analysis of long-term electroencephalographic signals is the most reliable technique, automatic seizures detection can help the physicians in comparing seizures and extracting common patterns. In this paper, a new approach to classify background activity and pre-ictal stereoelectroencephalographic signals is proposed. Linear and nonlinear features are extracted directly from the derived intrinsic mode functions of multivariate empirical mode decomposition technique and the classification is performed using support vector machines. The effectiveness of the proposed approach is evaluated using real datasets. Our results show good performance of the proposed approach since an accuracy of 100% is achieved using the first intrinsic mode function and a window size of 1024 samples. © 2020 IEEE

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

Chemical Composition and Antioxydant Activity of Laurus nobilis Floral Buds Essential Oil

The essential oil of Laurus nobilis floral buds (FB) collected in Tunisia was obtained by hydrodistillation and analysed by GC-MS. Twenty three constituents were identified. The main components of this oil were α-terpinyl acetate (28.43 %), methyl eugenol (19.57 %), eugenol (7.42 %) and elemicin (4.41 %). The antioxidant activity of the oil of Laurus nobilis FB was evaluated by two methods, β-carotene bleaching (BCB) test and 2.2-diphenyl-β-picrylhydrazyl (DPPH) assay using respectively butylated hydroxytoluene (BHT), trolox, gallic acid, caffeic acid and δ-tocopherol as standards. It was found that the Laurus nobilis FB oil have a significant antioxidant effect when tested by each method respectively. The antioxidant activity of this oil is more effective than the synthetic antioxidant (BHT) at 200 ppm and can be attributed to the active compounds eugenol, elemicin and methyl eugenol present in this essential oil

Classification of High Frequency Oscillations in intracranial EEG signals based on coupled time-frequency and image-related features

International audienceHigh Frequency Oscillations (HFOs) occurring in the range of [30–500 Hz] in epileptic intracranial ElectroEncephaloGraphic (iEEG) signals have recently proven to be good biomarkers for localizing the epileptogenic zone. Identifying these particular cerebral events and their discrimination from other transient events like interictal epileptic spikes is traditionally performed by experts through a visual inspection. However, this is laborious, very time-consuming and subjective. In this paper, a new classification approach of HFOs is proposed. This approach mainly relies on the combination of raw time frequency (TF) features, computed from a TF representation of HFOs using S-transform, with relevant image-based ones derived from a binarization of the corresponding TF grayscale image. The obtained feature vector is then used to learn a multi-class Radial Basis Function (RBF) based Support Vector Machine (SVM) classifier. The efficiency of the proposed approach, compared to conventional classification schemes based only on time, frequency or energy-based features, is confirmed, using both simulated and real iEEG signals. The proposed classification system has achieved, using simulated data and a signal to noise ratio (SNR) of 15 dB, a sensitivity, specificity, accuracy, area under the curve and F1-score around 0.990, 0.996, 0.995, 0.993 and 0.990 respectively. Besides, for real data, our proposed approach has attained the scores of 0.765, 0.941, 0.906, 0.929 and 0.768 for sensitivity, specificity, accuracy, area under the curve and F1-score respectively. These results confirm the relevance of coupling TF and image-related features, in the way proposed in this paper, for higher HFOs classification quality compared to already existing approaches. © 2021 Elsevier Lt