國立虎尾科技大學 |

Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features./
Author:	Jagadeesha, Raghu.
Description:	1 online resource (79 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-05.
Contained By:	Masters Abstracts International56-05(E).
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9781369885569

Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features.
Jagadeesha, Raghu.

Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features. - 1 online resource (79 pages)

Source: Masters Abstracts International, Volume: 56-05.

Thesis (M.S.)

Includes bibliographical references

Diagnosis of epilepsy or epileptic transients AEP (Abnormal Epileptiform Paroxysmal) is tedious, but important, and an expensive process. The process involves trained neurologists going over the patient's EEG records looking for epileptiform discharge like events and classifying it as AEP (Abnormal Epileptiform Paroxysmal) or non-AEP. The objective of this research is to automate the process of detecting such events and classifying them into AEP(definitely an Epileptiform Transient) and non-AEPs (unlikely an epileptiform transient). The problem is approached in two separate steps and cascaded to validate and analyze the performance of the overall system.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369885569Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features.
LDR:03178ntm a2200397Ki 4500 001 910719
005 20180517112608.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9781369885569
035 $a (MiAaPQ)AAI10257892
035 $a (MiAaPQ)clemson:14230
035 $a AAI10257892
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Jagadeesha, Raghu. $3 1182154
245 1 0 $a Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features.
264 0 $c 2017
300 $a 1 online resource (79 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 56-05.
500 $a Adviser: Robert Schalkoff.
502 $a Thesis (M.S.) $c Clemson University $d 2017.
504 $a Includes bibliographical references
520 $a Diagnosis of epilepsy or epileptic transients AEP (Abnormal Epileptiform Paroxysmal) is tedious, but important, and an expensive process. The process involves trained neurologists going over the patient's EEG records looking for epileptiform discharge like events and classifying it as AEP (Abnormal Epileptiform Paroxysmal) or non-AEP. The objective of this research is to automate the process of detecting such events and classifying them into AEP(definitely an Epileptiform Transient) and non-AEPs (unlikely an epileptiform transient). The problem is approached in two separate steps and cascaded to validate and analyze the performance of the overall system.
520 $a The first step is a detection problem to find the Epileptiform like transients (ETs) from the Electroencephalograph (EEG) of a patient. A Radial basis function-based neural network has been trained using a training set consisting of examples from both classes (ETs and non-ETs). The ETs are the yellow boxes which are marked by expert neurologists. There are no particular examples of non-ETs and any data not annotated by experts can be considered to be examples of non-ETs.
520 $a The second step is classification of the detected ETs also known as yellow boxes, into AEPs or non-AEPs. A similar Radial basis function-based neural network has been trained using the ETs marked and classified into AEPs and non-AEPs manually by seven expert neurologists. The annotations or yellow boxes along with the contextual signal was used to extract features using the Hilbert Huang Transform.
520 $a The system is validated by considering an entire epoch of the patient EEG and potential ETs are identified using the detector. The potential ETs marked by the detector are classified into AEPs and non-AEPs and compared against the annotations marked by the experts.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Artificial intelligence. $3 559380
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0800
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Clemson University. $b Electrical & Computer Engineering. $3 1179660
773 0 $t Masters Abstracts International $g 56-05(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10257892 $z click for full text (PQDT)