A5025401492- EEG and Brain-Computer Interfaces
- Epilepsy research and treatment
- Blind Source Separation Techniques
- Cell Image Analysis Techniques
- AI in cancer detection
- Brain Tumor Detection and Classification
- Neural Networks and Applications
- Advanced Memory and Neural Computing
- Fractal and DNA sequence analysis
- Neurological disorders and treatments
- Phonocardiography and Auscultation Techniques
- Neural dynamics and brain function
- ECG Monitoring and Analysis
- Neuroscience and Neuropharmacology Research
- Image Processing Techniques and Applications
Université Clermont Auvergne
2024
Institut Pascal
2024
Centre National de la Recherche Scientifique
2024
University of Sfax
2020-2023
Inserm
2021
Université de Rennes
2021
Recently, several studies have proved that High Frequency Oscillations (HFOs) of [80500] Hz are reliable biomarkers for delineating the epileptogenic zone. The total duration HFOs is extremely short compared to entire EEG dataset be analyzed. Therefore, visual marking time-consuming and laborious process. In order promote clinical use oscillations as tissue conduct large-scale investigations on cerebral activities, automatic detection techniques been proposed over past few years. present...
Microscopic image segmentation (MIS) plays a pivotal role in various fields such as medical imaging and biology. With the advent of deep learning (DL), numerous methods have emerged for automating improving accuracy this crucial analysis task. This systematic literature review (SLR) aims to provide an exhaustive overview state-of-the-art DL employed microscopic images. In review, we analyze diverse array studies published last five years, highlighting their contributions, methodologies,...
Automatic detection of epileptic seizures is a very crucial step for diagnosing patients with drug-resistant epilepsies. If visual analysis long-term electroencephalographic signals the most reliable technique, automatic can help physicians in comparing and extracting common patterns. In this paper, new approach to classify background activity pre-ictal stereoelectroencephalographic proposed. Linear nonlinear features are extracted directly from derived intrinsic mode functions multivariate...
In the past few years, visual detection of High Frequency Oscillations (HFOs) has led to a good understanding fundamental neural mechanisms underlying epileptic phenomena. However, marking HFOs is very tedious, extremely time consuming and remains subjective process. addition, some factors like low signal-to-noise ratio presence artifacts in electroencephalographic signals make scoring difficult task. Therefore, automatic detectors have been developed by different research groups with goal...
Cerebral High Frequency Oscillations (HFOs) have recently been discovered in epileptic EEG recordings. HFOs defined as spontaneous rhythmic oscillations with short duration, operating approximately the frequency range between 80 Hz and 500Hz. considered reliable precise biomarkers for delineating epileptogenic tissue. Also, a profound impact understanding cerebral mechanisms involved generation of seizures. Therefore, several algorithms detection different performance computational...