Dr. Seyyed Ali | Zendehbad University of Mazandaran | Iran
Dr. Seyyed Ali Zendehbad is a multidisciplinary researcher specializing in biomedical signal processing, cognitive computational neuroscience, and neurorehabilitation technologies. His research integrates deep learning, pattern recognition, and multimodal biological data modeling to enhance fatigue detection and neurorehabilitation systems. He has authored several peer-reviewed papers in reputable journals such as Scientific Reports, IEEE Access, Sensing and Bio-Sensing Research, and Healthcare Technology Letters, focusing on hybrid AI frameworks, EMG signal processing, and muscle synergy-based biofeedback mechanisms. Dr. Zendehbad’s work on developing intelligent rehabilitation systems, including his models like TraxVBF and FatigueNet, contributes to advancing telemonitoring and assistive technologies for neurological recovery. His scholarly output includes more than 25 documents, over 600 citations, and an h-index of 12, reflecting his growing impact in computational neuroscience and biomedical engineering. Recognized for innovation, he has achieved first-place awards in multiple national startup competitions and was honored with the Best Poster Award at the Congress of Neurology and Clinical Electrophysiology of Iran. As a postdoctoral researcher at the University of Mazandaran, his ongoing work emphasizes integrating trustworthy AI into telehealth systems, promoting equitable and efficient digital healthcare delivery through interdisciplinary research and technological innovation.
Featured Publications
1. Mazrooei Rad, E., Mazinani, S. M., & Zendehbad, S. A. (2025). Diagnosis of Alzheimer’s disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model. Computer Methods and Programs in Biomedicine Update, 5, 100192.
2. Zendehbad, S. A., Sharifi Razavi, A., Tabrizi, N., & Sedaghat, Z. (2025). A systematic review of artificial intelligence techniques based on electroencephalography analysis in the diagnosis of epilepsy disorders: A clinical perspective. Epilepsy Research, 207, 107582.
3. Mazrooei Rad, E., Zendehbad, S. A., & Hosseinzadeh, V. (2025). Fetal QRS complex detection based on adaptive filters and peak detection. Research on Biomedical Engineering, 41(3), 424–438.
4. Zendehbad, S. A., Ghasemi, J., & Samsami Khodadad, F. (2025). FatigueNet: A hybrid graph neural network and transformer framework for real-time multimodal fatigue detection. Scientific Reports, 15(1), 640.
5. Safdel, A., Zendehbad, S. A., & Ghasemi, J. (2025). Advanced deep learning approaches for accurate and efficient suspicious behavior detection in surveillance videos. Computational Sciences and Engineering, 21(2), 1099.