Research interests

My research interests span a wide spectrum of innovative computational solutions across domains such as:

• Creating and optimizing clinical decision support systems to accurately detect diseases, assist providers, and improve patient outcomes.
• Generating synthetic training data using generative adversarial networks (GANs) to augment limited real-world datasets for machine learning models.
• Developing seizure detection and prediction systems powered by analysis of electroencephalographic (EEG) signals.
• Analyzing kinematic phenotypes and biomechanics measures in patient populations qualifying for orthopedic surgeries such as knee osteoarthroplasty. This can help prioritize patients, optimize surgical approach selection, and inform rehabilitative needs.
• Recognizing emotional states and sentiments from social media data using natural language processing approaches.
• Uncovering early behavioral markers of neurological disorders such as autism spectrum disorder using EEG.
• Detecting illnesses at the earliest stages by using infants’ cry signal processing
• Advancing speech pattern recognition technologies
• Applying ECG analysis for heart diseases detection and prediction.

Selected publications

1. L. Abou-Abbas, K. Henni, I. Djemal, A. Mitiche and N. Mezghani. (2023) “Patient-Independent Epileptic Seizure Detection by Stable Feature Selection”. Expert Systems with Applications, 120585.
2. L. Abou-Abbas, K. Henni, I. Jemal, A. Mitiche and N. Mezghani. “Analyzing US Airline Customer Sentiment on Twitter using Multinomial Logistic Regression and Feature Reduction”. Accepted IEEE (2023).
3. Jemal, I., Abou-Abbas, L., Mitiche, A., Henni, K., & Mezghani, N. (2023) “Domain Adaptation for EEG-based Cross-subject Epileptic Seizure Prediction?” Frontiers in Neuroinformatics, 18, 1303380.
4. L. Abou-Abbas, Jemal, I., Henni, K., Ouakrim, Y., Mitiche, A., & Mezghani, N. (2022) “EEG Oscillatory Power and Complexity for Epileptic Seizure Detection”. Applied Sciences, 12(9), 4181.
5. Jemal, I., Abou-Abbas, L., Mitiche, A., & Mezghani, N. (2022). « An Explainable Deep Learning Model for Epileptic Seizure Prediction using EEG Signals » IEEE Access, 10, 60141-60150.
6. L. Abou-Abbas, van Noordt S., Elsabbagh M. (2021). “Event Related Potential Analysis Using Machine Learning to Predict Diagnostic Outcome of Autism Spectrum Disorder” In Bioengineering and Biomedical Signal and Image Processing: First International Conference, BIOMESIP 2021, Meloneras, Gran Canaria, Spain, July 19-21, 2021, Proceedings 1. Springer International Publishing, 2021
7. L. Abou-Abbas, Jemal I., Henni K., Mitiche A., Mezghani N. (2021) “Focal and Generalized Seizures Distinction by Rebalancing Class Data and Random Forest Classification” In Bioengineering and Biomedical Signal and Image Processing: First International Conference, BIOMESIP 2021, Meloneras, Gran Canaria, Spain, July 19-21, 2021, Proceedings 1 (pp. 63-70). Springer International Publishing.
8. L. Abou-Abbas, J.Desjardins, and M. Elsabbagh (2021) “Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder”. Brain Sciences, 11(4), 409.
9. Jemal, I., Mitiche, A., Abou-Abbas, L., Henni, K., & Mezghani, N. (2021) “An Effective Deep Neural Network Architecture for Cross-Subject Epileptic Seizure Detection in EEG Data” In the 11th International Conference on Electronics, Communications and Networks (CECNet), November 18-21, 2021. Vol. 345. IOS Press, 2022.
10. van Noordt, S.J.R., Desjardins, J.A., Huberty, S., Abou-Abbas, L., Webb, S.J., Levin, A., Segalowitz, S.J., Evans, A.C., The BASIS Team, & Elsabbagh, M. (2020) “EEG-IP: An international infant EEG data integration platform for the study of risk and resilience in autism and related conditions”. Molecular Medicine, 26(1), 1-11.
11. L. Abou-Abbas, C. Tadj, and H. Fersaie Alaie (2017) “A Fully Automated Approach for Babies’ Cry Sounds Segmentation in a Realistic Clinical Environment and Boundary Detection of Corresponding Expiratory and Inspiratory Episodes” Journal of the Acoustical Society of America, 142(3), 1318-1331.
12. L. Abou-Abbas, C. Tadj, C. Gargour, L. M. Jouibari (2016) “Expiratory and Inspiratory Cries Detection using different Signals’ Decomposition Techniques” Journal of voice, 31(2), 259-e13.
13. H. F. Alaie, L. Abou-Abbas, C. Tadj (2016)” Cry-Based Infant Pathology Classification Using GMMs” - Speech communication, 77, 28-52.
14. L. Abou-Abbas, H. Fersaie Alaie, and C. Tadj (2015)”Automatic Detection of the Expiratory and Inspiratory Phases in Newborn Cry Signals” Biomedical signal processing and control, 19, 35-43.
15. L. Abou-Abbas, L. M. Jouibari, C. Gargour, C. Tadj (2015) “On the use of EMD for Automatic Newborn Cry Segmentation,” in third international conference on advances in Biomedical Engineering (ICABME), pp. 262-265. IEEE.
16. L. Abou-Abbas, H. F. Alaei, and C. Tadj, “Segmentation of Voiced Newborns’ Cry Sounds using Wavelet Packet Based Features,” in Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference, Halifax, Canada on May 2015, pp. 796-800.

Academic degrees

Ph.D. in engineering, Signal Processing and Machine Learning, Ecole de Technologie Supérieure-Montreal, Canada, 2016
M.S., Network of Telecommunication, Ecole de Technologie Supérieure, Montreal, Canada, 2012 (Fast-Track Transition from Master to PhD)
M.S., Network of Telecommunication, Lebanese University and St-Joseph University, Lebanon, 2008
B.E., Electrical and Electronic Engineering- Section: Communication and Computer Engineering, Lebanese University, Lebanon, 2005

Postdoctoral Experience

Postdoctoral Fellowship, Neurology and Neurosurgery department at McGill University and Montreal Neurological Institute, Canada, January 2017 to April 2020
Postdoctoral Fellowship, Science and Technology department at TELUQ University and The University of Montreal Hospital Research Center (CRCHUM), Canada, September 2020 to October 2023

Selected Grants

2018-2022 - Postdoctoral research grants (B3X) - Quebec Research Fund - Nature and Technologies (FRQNT) for the project “EEG biomarker for risk and diagnosis of autism spectrum disorders”
2019 - Transforming Autism Care Consortium (TACC) Quebec Autism Research Training (QART) Program grant for the project “EEG markers for risk and diagnosis of autism spectrum disorder” (Declined due to accumulation of other funding)
2022- 2023 Mitacs Acceleration grant for the project “Development of a decision-making tool for knee arthroplasty planning using AI methods incorporating functional measures of gait.”