Prof. Lotfi Chaari, Neurosciences, Best Researcher Award
Professor at Toulouse INP, France
Dr. Lotfi CHAARI, a French citizen and Associate Professor HDR at Institut National Polytechnique de Toulouse (Toulouse INP), is a distinguished researcher specializing in signal and image processing, artificial intelligence, and machine learning. With an extensive background and expertise, he has made notable contributions to biomedical signal processing and remote sensing.
Author Metrics:
His excellence is evident through notable author metrics, including the receipt of the Best Paper Award in 2022 and achieving the status of IEEE Senior Member in 2019, highlighting his significant impact on the field.
Citations:
Total Citations: 5048
h-index:
h-index: 24
Explanation: The h-index is a measure of both the productivity and impact of a researcher’s work. An h-index of 24 means that the researcher has published at least 24 papers that have each been cited at least 24 times.
i10-index:
i10-index: 32
Explanation: The i10-index is specific to Google Scholar and represents the number of publications (articles, conference papers, etc.) by the researcher that have at least 10 citations each. An i10-index of 32 indicates that the researcher has 32 publications with at least 10 citations each.
Education:
Dr. CHAARI earned his Ph.D. in Signal and Image Processing from the University of Paris-Est Marne-la-Vallée in 2010. Subsequently, he completed a post-doctoral dissertation (HDR) at Toulouse INP in 2017.
Research Focus:
His research interests encompass a broad spectrum, including artificial intelligence, machine learning, deep learning, anomaly detection, pattern recognition, and biomedical signal and image processing. Dr. CHAARI has a particular focus on variational and Bayesian optimization, as well as inverse problems, restoration, and enhancement.
Professional Journey:
Since 2012, Dr. CHAARI has served as an Associate Professor at Toulouse INP, contributing significantly to the fields of artificial intelligence and machine learning. Prior to this, he gained valuable experience as a post-doctoral fellow at INRIA Grenoble-Rhône Alpes from 2010 to 2012.
Honors & Awards:
His contributions have been recognized with prestigious honors and awards, including the HOPE Best Workshops Paper in 2023, the Nutrients 2022 Best Paper Award, and his elevation to the grade of IEEE Senior Member in 2019. Additionally, he holds a position on the National Representative Board for EPMA.
Publications Top Noted & Contributions:
Dr. CHAARI has actively contributed to reputable journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, and IEEE Transactions on Medical Imaging. He has also played a crucial role in conferences, including IEEE ICIP, IEEE ICASSP, EUSIPCO, and ISBI.
The impact of COVID-19 home confinement on various aspects of individuals’ lives has been extensively investigated through the ECLB-COVID19 international online survey. The results of these studies, conducted by researchers such as A. Ammar, M. Brach, K. Trabelsi, H. Chtourou, O. Boukhris, L. Masmoudi, and others, have been published in several scientific journals:
The abstract you provided describes a study focused on the detection of Mild Cognitive Impairment (MCI) using EEG and HRV (Heart Rate Variability) data.
Objective: The study aims to develop a safe and effective method for early detection of Mild Cognitive Impairment, a condition often linked to neurodegeneration and aging, which may progress to Alzheimer’s disease.
Methodology:
- The study uses a dataset comprising EEG and HRV data from 15 subjects, randomly assigned to training and testing groups of healthy controls (HC) and MCI patients.
- Raw EEG and HRV data are analyzed to extract various features, including time, frequency, and non-linear features.
- A scaling step is employed to address significant disparities between features.
Machine Learning Models: Five machine learning models are evaluated for the classification task: Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). A hybrid machine learning model with a voting system is developed, combining the top-performing models for enhanced accuracy.
Results: The experimental findings demonstrate the efficacy of the proposed technique:
- An average accuracy of 93.86% is achieved.
- Sensitivity (true positive rate) is 93.87%.
- Specificity (true negative rate) is 93.53%.
Conclusion: The study concludes that the developed method, utilizing a hybrid machine learning model, is effective in categorizing MCI and healthy control patients during the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) task using EEG and HRV multimodal data. The first CERAD test is highlighted as a novel biomarker for early MCI identification.
Significance: The findings suggest that combining EEG and HRV signals in a multimodal approach, along with machine learning models, can offer a reliable and efficient method for early detection of Mild Cognitive Impairment, providing a potential avenue for timely intervention and treatment.
- Authors: A. Ammar, M. Brach, K. Trabelsi, H. Chtourou, O. Boukhris, L. Masmoudi, et al.
- Published in: Nutrients, 12 (6), 1583–2314, 2020
- Focus: Investigates the impact of COVID-19 home confinement on eating behavior and physical activity.
- Authors: A. Ammar, H. Chtourou, O. Boukhris, K. Trabelsi, L. Masmoudi, M. Brach, et al.
- Published in: International Journal of Environmental Research and Public Health, 17 (17), 6237–452, 2020
- Focus: Explores the global impact of COVID-19 home confinement on social participation and life satisfaction.
- Authors: A. Ammar, K. Trabelsi, M. Brach, H. Chtourou, O. Boukhris, L. Masmoudi, et al.
- Published in: Biology of Sport, 38 (1), 9–21, 2021
- Focus: Examines the effects of home confinement on mental health and lifestyle behaviors during the COVID-19 outbreak.
“Psychological consequences of COVID-19 home confinement: The ECLB-COVID19 multicenter study”
- Authors: A. Ammar, P. Mueller, K. Trabelsi, H. Chtourou, O. Boukhris, L. Masmoudi, et al.
- Published in: PloS One, 15 (11), e0240204, 2020
- Focus: Investigates the psychological consequences of COVID-19 home confinement.
- Authors: K. Trabelsi, A. Ammar, L. Masmoudi, O. Boukhris, H. Chtourou, B. Bouaziz, et al.
- Published in: Biology of Sport, 38 (4), 495–506
- Focus: Examines the worldwide changes in sleep patterns and physical activity levels during COVID-19 confinement in a large sample of 5056 individuals.
Research Timeline:
Commencing his research journey in 2010 with a post-doctoral fellowship, Dr. CHAARI has steadily progressed, becoming an Associate Professor at Toulouse INP in 2012. Over the years, his research has consistently focused on advancing knowledge in signal and image processing, artificial intelligence, and machine learning.