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Hardware software co-design of epileptic seizure prediction systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hardware software co-design of epileptic seizure prediction systems by Shiva Maleki Varnosfaderani, Nabil J. Sarhan, Mohammad Alhawari.
Author:	Varnosfaderani, Shiva Maleki.
other author:	Sarhan, Nabil J.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xviii, 98 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	ConvulsionsForecasting.
Online resource:	https://doi.org/10.1007/978-3-031-95357-6
ISBN:	9783031953576$q(electronic bk.)

Hardware software co-design of epileptic seizure prediction systems
Varnosfaderani, Shiva Maleki.

Hardware software co-design of epileptic seizure prediction systems [electronic resource] /by Shiva Maleki Varnosfaderani, Nabil J. Sarhan, Mohammad Alhawari. - Cham :Springer Nature Switzerland :2025. - xviii, 98 p. :ill. (chiefly col.), digital ;24 cm. - Analog circuits and signal processing,2197-1854. - Analog circuits and signal processing..

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

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. Provides thorough description of the procedures involved in creating an effective epilepsy prediction model; Offers an overview of the seizure disorder, the many types of seizures, their symptoms, and more; Covers deep learning-based prediction systems, available datasets and effective epileptic machine learning model design.

ISBN: 9783031953576$q(electronic bk.)

Standard No.: 10.1007/978-3-031-95357-6doiSubjects--Topical Terms:

1003810
Convulsions
--Forecasting.

LC Class. No.: RC372

Dewey Class. No.: 616.853

Hardware software co-design of epileptic seizure prediction systems
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