Currently, I am working as a Graduate Research Assistant at Le Laboratoire d’Imagerie, de Vision et d’Intelligence Artificielle (Livia) within École de Technologie Supérieure. My focus lies in my master’s thesis, which delves into the realm of depression detection by analyzing the intricate interactions between the brain and heart. Leveraging neuroscientific principles and advanced deep learning algorithms, I’m exploring real polysomnography data obtained from sleep clinics.
In this capacity, my primary responsibilities entail conducting comprehensive statistical analyses on the interconnected features of heart-brain interactions to identify depression bio-markers in patients. I’m also harnessing polysomnography data as inputs for cutting-edge time series algorithms, such as RNNs, LSTMs, and Transformers, to distinguish patients with depression from healthy cases.
Research interests:
- Advanced biomedical signal processing
- Deep learning
- Neuroscience
- Statistical analysis
Name of advisors:
- Professor Mohamad Forouzanfar
- Professor Rebecca Robillard
- Professor Jean-marc Lina
GitHub:
List of publications:
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- Azad, M. H., Farzam, R., Sadeghi, H., Yussefian, N. Z., & Forouzanfar, M. (2021, June). Toward continuous estimation of cardiorespiratory parameters in oscillometry: A simulation study. In 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-5). IEEE.
- Azad, M.H., Robillard, R., Higginson, C., Lina, J.M., Forouzanfar, M., Unraveling Sleep EEG-ECG Interactions in Major Depression: Preliminary Results of a Coherence Analysis. SLEEP 2024, the 38th annual meeting of the APSS
Research & Innovation
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