Detection of epileptic seizure in EEG signals using linear least squares preprocessing

Highlights • Linear least squares preprocessing models are developed for automatic detection of seizures. • They extract key features of an epileptic EEG signal. • They significantly reduce the dimension of the classification problem and the computational time. • They enhance the classification accu...

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Bibliographic Details
Published inComputer methods and programs in biomedicine Vol. 133; pp. 95 - 109
Main Author Zamir, Z. Roshan
Format Journal Article
LanguageEnglish
Published Ireland Elsevier B.V 01.09.2016
Subjects
Accuracy
Biological signal classification
Classification
Classifiers
Data analysis
EEG seizure detection
Electroencephalography
Electroencephalography - methods
Epilepsy - physiopathology
Feature extraction
Humans
Internal Medicine
Least-Squares Analysis
Linear least squares problems
Mathematical models
Models, Theoretical
Other
Preprocessing
Seizing
Signal approximation
92C55
Signal approximation
Data analysis
65K05
Biological signal classification
EEG Seizure detection
90C25
Feature extraction
65D15
65D07
Linear least squares problems
EEG seizure detection
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Summary:Highlights • Linear least squares preprocessing models are developed for automatic detection of seizures. • They extract key features of an epileptic EEG signal. • They significantly reduce the dimension of the classification problem and the computational time. • They enhance the classification accuracy of an EEG signal in presence of seizures. • They are robust and efficient for detecting epileptic seizures.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2016.05.002