Paper Info
Title
A Distributed Descriptor Characterizing Structural Irregularity of EEG Time Series for Epileptic Seizure Detection.
Abstract
This paper presents a novel descriptor aiming at anomaly detection in sequential data, like epileptic seizure detection with EEG time series. The descriptor is derived from the eigenvalue decomposition (EVD) of a Hankel-form data matrix generated from the raw time series. Simulation trials imply that the descriptor is capable of characterizing the structural aspect of a time series. In addition, we deploy the proposed descriptor as a feature extractor and apply it on Bonn Seizure Database which is widely used in seizure detection. The high accuracies on classification problems are comparable with the state-of-the-art so validate the effectiveness of our method.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512919
EMBC
Field
DocType
Volume
Anomaly detection,Computer vision,Time series,Pattern recognition,Computer science,Matrix decomposition,Feature extraction,Epileptic seizure,Time–frequency analysis,Artificial intelligence,Eigendecomposition of a matrix,Electroencephalography
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Zhenning Mei100.68
Xian Zhao201.69
Hongyu Chen312.09