EEG seizure detection and prediction algorithms: a survey

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1687-6180

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: EEG seizure detection and prediction algorithms: a survey ; volume:2014 ; number:1 ; day:23 ; month:12 ; year:2014 ; pages:1-21 ; date:12.2014
EURASIP journal on advances in signal processing / European Association for Speech, Signal and Image Processing ; 2014, Heft 1 (23.12.2014), 1-21, 12.2014

Creator: Alotaiby, Turkey N.
Alshebeili, Saleh A.
Alshawi, Tariq
Ahmad, Ishtiaq
Abd El-Samie, Fathi E.

Contributor: SpringerLink (Online service)

DOI: 10.1186/1687-6180-2014-183

URN: urn:nbn:de:101:1-2021082806351146048580

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:50 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Alotaiby, Turkey N.
Alshebeili, Saleh A.
Alshawi, Tariq
Ahmad, Ishtiaq
Abd El-Samie, Fathi E.
SpringerLink (Online service)

Other Objects (12)

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

EEG epilepsy seizure prediction: the post-processing stage as a chronology

On the clinical acceptance of black‐box systems for EEG seizure prediction

Classifier combination supported by the sleep-wake cycle improves EEG seizure prediction performance

Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic

The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions

TATPat based explainable EEG model for neonatal seizure detection

A multi-context learning approach for EEG epileptic seizure detection

Epileptic seizure detection from EEG signals using logistic model trees

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

EEG epilepsy seizure prediction: the post-processing stage as a chronology

On the clinical acceptance of black‐box systems for EEG seizure prediction

Classifier combination supported by the sleep-wake cycle improves EEG seizure prediction performance

Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic

The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions

TATPat based explainable EEG model for neonatal seizure detection

A multi-context learning approach for EEG epileptic seizure detection

Epileptic seizure detection from EEG signals using logistic model trees

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

EEG epilepsy seizure prediction: the post-processing stage as a chronology

On the clinical acceptance of black‐box systems for EEG seizure prediction

Classifier combination supported by the sleep-wake cycle improves EEG seizure prediction performance

Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic

The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions

TATPat based explainable EEG model for neonatal seizure detection

A multi-context learning approach for EEG epileptic seizure detection

Epileptic seizure detection from EEG signals using logistic model trees

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

EEG seizure detection and prediction algorithms: a survey

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

EEG epilepsy seizure prediction: the post-processing stage as a chronology

EEG epilepsy seizure prediction: the post-processing stage as a chronology

On the clinical acceptance of black‐box systems for EEG seizure prediction

Classifier combination supported by the sleep-wake cycle improves EEG seizure prediction performance

Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic

The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions

TATPat based explainable EEG model for neonatal seizure detection

A multi-context learning approach for EEG epileptic seizure detection

Epileptic seizure detection from EEG signals using logistic model trees

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

EEG epilepsy seizure prediction: the post-processing stage as a chronology

EEG epilepsy seizure prediction: the post-processing stage as a chronology

On the clinical acceptance of black‐box systems for EEG seizure prediction

Classifier combination supported by the sleep-wake cycle improves EEG seizure prediction performance

Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic

The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions

TATPat based explainable EEG model for neonatal seizure detection

A multi-context learning approach for EEG epileptic seizure detection

Epileptic seizure detection from EEG signals using logistic model trees

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

EEG epilepsy seizure prediction: the post-processing stage as a chronology

EEG epilepsy seizure prediction: the post-processing stage as a chronology

On the clinical acceptance of black‐box systems for EEG seizure prediction

Classifier combination supported by the sleep-wake cycle improves EEG seizure prediction performance

Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic

The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions

TATPat based explainable EEG model for neonatal seizure detection

A multi-context learning approach for EEG epileptic seizure detection

Epileptic seizure detection from EEG signals using logistic model trees

Related objects

Reset password