Paper Info
Title
Dynamic Mode Decomposition Based Epileptic Seizure Detection from Scalp EEG.
Abstract
Reliable detection of the onset of epileptic seizures has seen renewed interest over the past few years, owing to several factors including, the global push toward digital health-care, the advancements in signal processing techniques, and the increased computational power of machines. A reliable automatic system could result in tremendous improvement in the quality of life of epilepsy patients. This paper presents dynamic mode decomposition (DMD), a data-driven dimensionality reduction technique, originally used in fluid mechanics, as an instrument for epileptic seizure detection from scalp electroencephalograph (EEG) data. DMD is employed in this paper to measure power of signals in different frequency bands. These subband-powers, along with signal curve lengths, are used as features for training random under-sampling boost decision-tree classifier. Post-processing measures ensure an acceptable balance between false positives and true positives. The proposed algorithm has been tested over a thousand hours of EEG data from two different data sets, the CHB-MIT data set and the KU Leuven data set, giving sensitivity values of 0.87 and 0.88, respectively, and specificity values of 0.99 for both the data sets.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2853125
IEEE ACCESS
Keywords
Field
DocType
Biomedical signal processing,EEG,epileptic seizure detection,dynamic mode decomposition,RUSBoost,decision trees
Dynamic mode decomposition,Signal processing,Data set,Dimensionality reduction,Pattern recognition,Computer science,Feature extraction,Epileptic seizure,Artificial intelligence,Electroencephalography,Distributed computing,False positive paradox
Journal
Volume
ISSN
Citations 
6
2169-3536
1
PageRank 
References 
Authors
0.35
0
5
Name
Order
Citations
PageRank
Muhammad Sohaib J. Solaija121.40
Sajid Saleem25912.80
Khawar Khurshid383.84
Syed Ali Hassan433662.47
Awais M. Kamboh5267.01