Murtadha Dawood Hssayeni
Computer Engineering Department
Lecturer
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Murtadha D. Hssayeni is an Assistant Professor at University of Technology-Baghdad (UoT-B), Iraq. He was born in Babil, Iraq, in 1990. He received a B.Sc. degree in Computer Engineering from UoT-B, in 2012, and an M.Sc. degree in the same field from Rochester Institute of Technology (RIT), Rochester, NY, in 2017. In 2022, he received a Ph.D. degree in Computer Engineering from Florida Atlantic University (FAU), FL, and became a postdoctoral fellow. During his studies, he worked in multiple biomedical signal and image analysis laboratories, and published several research papers in the field, making his code and collected data publicly available. His research interests include biomedical signal and image processing, tensor decomposition, and machine learning, especially deep learning. The author's work covers various areas, including continuous estimation of Parkinson's Disease symptoms, activity recognition, detection of cardiac dysfunction, and intracranial hemorrhage segmentation.
Ph.D. in Computer Engineering from Florida Atlantic University, USA 2022
Biomedical signal and image processing, tensor decomposition, and machine learning, especially deep learning.
-M., Hssayeni, M. & Ghoraani, B. (2024). Deep regression modeling for imbalanced and incomplete time-series data. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI).
-Davidashvilly, S., Cardei, M., Hssayeni, M., Chi, C., & Ghoraani, B. (2024). Deep neural networks for wearable sensor-based activity recognition in Parkinson's disease: investigating generalizability and model complexity. BioMedical Engineering OnLine, 23(1), 1-24.
-Chan, J. Y., Hssayeni, M. D., Wilcox, T., & Ghoraani, B. (2023). Exploring the feasibility of tensor decomposition for analysis of fNIRS signals: a comparative study with grand averaging method. Frontiers in Neuroscience, 17, 1180293.
-Hssayeni, Murtadha D., et al. ""Dyskinesia Estimation of Imbalanced Data Using a Deep-Learning Model."" 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022.
-Hssayeni, Murtadha D. Deep Learning Regression Models for Limited Biomedical Time-Series Data. Diss. Florida Atlantic University, 2022.
-Hssayeni, Murtadha D., et al. ""The forecast of COVID-19 spread risk at the county level."" Journal of big data 8.1 (2021): 1-16.
-Hssayeni, Murtadha D., et al. ""Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks."" Scientific reports 11.1 (2021): 1-12.
-Hssayeni, Murtadha D., et al. ""Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III."" Biomedical engineering online 20.1 (2021): 1-20.
-Hssayeni, M. D. et al. (2021). Multi-Modal Physiological Data Fusion for Affect Estimation Using Deep Learning. IEEE Access, 9, 21642-21652.
-Ghoraani, B., Boettcher, LN, Hssayeni, M. D., et al (2021). Detection of mild cognitive impairment and Alzheimer's disease using dual-task gait assessments and machine learning. Biomedical Signal Processing and Control 64, 102249.
-Hssayeni, M. D. et al. (2020). Intracranial Hemorrhage Segmentation Using A Deep Convolutional Model. Data Journal, 5, 14.
-Hssayeni, M. D. et al. (2019). Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements. Sensors, 19(19), 4215.
-Hssayeni, M. D. et al. (2019). Hybrid Feature Extraction for Detection of Degree of Motor Fluctuation Severity in Parkinson's Disease Patients. Entropy, 21(2), 137.
-Hssayeni, M. D. et al (2019). Assessment of Response to Medication in Individuals with Parkinson's disease. Medical engineering & physics, 67, 33-43.
-Ghoraani, B., Hssayeni, M. D., et al (2019). Multilevel Features for Sensor-based Assessment of Motor Fluctuation in Parkinson's Disease Subjects. IEEE journal of biomedical and health informatics.
Biometrics, Python, Microprocessor.
