Hardware Software Co-design of Epileptic Seizure Prediction Systems

Dr. Shiva Maleki Varnosfaderani is a faculty member in the Department of Electrical and Computer Engineering at Wayne State University. She completed her postdoctoral research in artificial intelligence applications for healthcare systems, continuing her commitment to enhancing patient-centered healthcare solutions through machine learning and integrated circuit design. She earned her Ph.D. in Electrical Engineering from Wayne State University in December 2023, focusing on machine learning-based epileptic seizure prediction using EEG data for implantable and wearable healthcare devices. Prior to that, she obtained her M.Sc. and B.Sc. degrees in Electrical Engineering from Isfahan University of Technology, where she specialized in the hardware implementation of face detection systems using wavelet networks.Dr. Maleki Varnosfaderani is proficient in applying machine learning to a range of domains, particularly in healthcare technologies and ASIC design for biomedical applications.Her research interests span machine learning, signal and image processing, biomedical integrated circuits, and AI-driven healthcare solutions. She has led multiple research projects on seizure prediction and the development of power-efficient AI models for medical devices. Her industry experience includes contributing to neuromodulation ASIC design, bridging academic innovation with practical applications.Dr. Nabil J. Sarhan is an Associate Professor of Electrical and Computer Engineering at Wayne State University and the Director of Wayne State Computer Systems and Deep Learning Research Laboratory. He is a co-director of the interdisciplinary M.S. Program in Artificial Intelligence. Dr. Sarhan received Ph.D. and M.S. degrees in Computer Science and Engineering at the Pennsylvania State University and a B.S. degree in Electrical Engineering at Jordan University of Science and Technology.Dr. Mohammad Alhawari is an Associate Professor in the Department of Electrical and Computer Engineering at Wayne State University. He served as a Post-doctoral Research Fellow at Khalifa University between 2016 and 2018. Dr. Alhawari holds a Ph.D. from Khalifa University (2016), a Master of Science from Masdar Institute (2012), and a Bachelor of Science from Yarmouk University (2008), all in Electrical and Computer Engineering. Since joining WSU in 2018, Dr. Alhawari has been a prominent faculty member in the field of chip design research, focusing on the development of integrated analog and mixed-signal circuits. As the director of the Intelligent Chips (iChip) research lab, he leads a world-class research program in Systems-on-Chip that aims to address the challenges facing emerging technologies in fields such as automotive, biomedical, Internet of Things, and wireless industries. iChip's research is driven by current and future industry needs, with a focus on providing innovative solutions that contribute to the development of a highly-skilled workforce in smart technologies. The lab aims to become a hub for integrated system solutions and foster partnerships with high-tech industries in the greater Detroit area and beyond. Dr. Alhawari's research at WSU is dedicated to advancing the field of chip design and helping to bring emerging technologies to market.Dr. Alhawari has achieved remarkable success in his career, receiving the prestigious 2023 NSF Career Award - the highest honor awarded to young faculty members. He also received the 2023 Faculty Research Excellence Award to recognize his research, Dr. Alhawari has authored or co-authored over 60 peer-reviewed publications of high impact. He has also authored one book and co-authored three book chapters, and has five granted patents with one pending. Dr. Alhawari's research group focuses on four main areas of study: Powering Future SoCs, AI Hardware, Smart Healthcare, and Advanced Communication Systems.

• What is Epilepsy?.- Importance of Epilepsy Prediction.- Available EEG Datasets.- Data Preparation.- Epilepsy Prediction Models.- Design Consideration for wearable and implantable epileptic prediction devices.