Hardware Software Co-design of Epileptic Seizure Prediction Systems
Springer International Publishing (Verlag)
978-3-031-95356-9 (ISBN)
This book offers insights into hardware-software co-design of epilepsy prediction models. This comprehensive exploration is a key to unlocking the mysteries of seizure forecasting, equipped with expert guidance and visionary foresight. From theory to practice, the authors illuminate the path forward, providing researchers with the tools and knowledge needed to navigate this dynamic field with confidence. They explore the latest advancements in deep learning technology and gain invaluable perspectives on the future landscape of epilepsy research. Bridging the gap between innovation and practicality, this book is a beacon for those seeking to make a tangible impact in healthcare.
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.
What is Epilepsy?.- Importance of Epilepsy Prediction.- Available EEG Datasets.- Data Preparation.- Epilepsy Prediction Models.- Design Consideration for wearable and implantable epileptic prediction devices.
| Erscheinungsdatum | 22.08.2025 |
|---|---|
| Reihe/Serie | Analog Circuits and Signal Processing |
| Zusatzinfo | XVIII, 98 p. 37 illus., 32 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Weitere Themen ► Hardware |
| Technik ► Elektrotechnik / Energietechnik | |
| Schlagworte | Artifact Removal Techniques • Deep learning techniques • Epilepsy prediction Models • Two-Layer LSTM • Wearable and Implantable Devices |
| ISBN-10 | 3-031-95356-8 / 3031953568 |
| ISBN-13 | 978-3-031-95356-9 / 9783031953569 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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