Dual tree complex wavelet transform based analysis of epileptiform discharges

Diagnosis of epileptic seizures entails visual inspection of complex seizure patterns which is a tedious task. Development of automated systems for analysing brain activity would significantly minimise the epilepsy treatment gap by providing assistance to neurophysiologists. Present research work is...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of information technology (Singapore. Online) Jg. 10; H. 4; S. 543 - 550
Hauptverfasser: Khan, Ayesha Tooba, Khan, Yusuf Uzzaman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Singapore 01.12.2018
Springer Nature B.V
Schlagworte:
Algorithms
Brain
Computer Communication Networks
Computer Science
Computer Systems Organization and Communication Networks
Convulsions & seizures
Discharge
Epilepsy
Feature extraction
Information Systems and Communication Service
Management of Computing and Information Systems
Original Research
Seizures
Software Engineering/Programming and Operating Systems
Wavelet analysis
Wavelet transforms
Dual tree complex wavelet transform
Seizure detection
EEG
K-fold cross validation
Classification
ISSN:2511-2104, 2511-2112
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Diagnosis of epileptic seizures entails visual inspection of complex seizure patterns which is a tedious task. Development of automated systems for analysing brain activity would significantly minimise the epilepsy treatment gap by providing assistance to neurophysiologists. Present research work is intended to provide insight to the epileptiform discharges during the seizures using dual tree complex wavelet transform. Algorithm is developed using publically available data from Bonn University. Statistical and nonlinear features, selected on the basis of Bhattacharyya distance, are extracted from EEG segments to demarcate the seizure and nonseizure EEG boundaries. Quadratic classification of EEG features followed by k-fold cross validation with varying train to test ratios is employed to develop a generalised robust model. Performance of classifier is accessed in terms of statistical parameters.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-018-0149-5