東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

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.
[NT 15003449]:	What is Epilepsy? -- Importance of Epilepsy Prediction -- Available EEG Datasets -- Data Preparation -- Epilepsy Prediction Models -- Design Consideration for wearable and implantable epileptic prediction devices.
Contained By:	Springer Nature eBook
Subject:	Convulsions - Forecasting. -
Online resource:	https://doi.org/10.1007/978-3-031-95357-6
ISBN:	9783031953576

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

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

3791673
Convulsions
--Forecasting.

LC Class. No.: RC372

Dewey Class. No.: 616.853

Hardware software co-design of epileptic seizure prediction systems
LDR:02328nmm a2200337 a 4500 001 2414799
003 DE-He213
005 20251001130501.0
006 m d
007 cr nn 008maaau
008 260205s2025 sz s 0 eng d
020 $a 9783031953576 $q (electronic bk.)
020 $a 9783031953569 $q (paper)
024 7 $a 10.1007/978-3-031-95357-6 $2 doi
035 $a 978-3-031-95357-6
040 $a GP $c GP
041 0 $a eng
050 4 $a RC372
072 7 $a TJFC $2 bicssc
072 7 $a TEC008010 $2 bisacsh
072 7 $a TJFC $2 thema
082 0 4 $a 616.853 $2 23
090 $a RC372 $b .V321 2025
100 1 $a Varnosfaderani, Shiva Maleki. $3 3791671
245 1 0 $a Hardware software co-design of epileptic seizure prediction systems $h [electronic resource] / $c by Shiva Maleki Varnosfaderani, Nabil J. Sarhan, Mohammad Alhawari.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xviii, 98 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Analog circuits and signal processing, $x 2197-1854
505 0 $a What is Epilepsy? -- Importance of Epilepsy Prediction -- Available EEG Datasets -- Data Preparation -- Epilepsy Prediction Models -- Design Consideration for wearable and implantable epileptic prediction devices.
520 $a 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.
650 0 $a Convulsions $x Forecasting. $3 3791673
650 0 $a Epilepsy $x Treatment $x Technological innovations. $3 3791674
650 1 4 $a Electronics Design and Verification. $3 3592716
650 2 4 $a Embedded Systems. $3 3592715
650 2 4 $a Digital and Analog Signal Processing. $3 3538815
700 1 $a Sarhan, Nabil J. $3 3791672
700 1 $a Alhawari, Mohammad. $3 3295933
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Analog circuits and signal processing. $3 1565978
856 4 0 $u https://doi.org/10.1007/978-3-031-95357-6
950 $a Engineering (SpringerNature-11647)