Manifold learning applied on EEG signal of the epileptic patients for detection of normal and pre-seizure States.
In this paper, several Manifold Learning (ML) techniques for dimension reduction of EEG feature vectors are introduced and applied on set of epileptic EEG signals. These include Principal Component Analysis (PCA), Multidimensional Scaling (MDS), Isometric Mapping (ISOMAP) and Locally Linear Embedding (LLE). While EEG signals of epileptic patients contain necessary information with regards to the various brain states of epileptic patients, for extraction of useful information in the EEG signals and for detection, often construction of high-dimensional feature vectors is utilized. Analysis of such high-dimensional feature vectors are complex and time consuming. This paper deals with dimension reduction of the extracted feature vectors and comparative analysis of the performance of several manifold learning techniques as applied on EEG signals of epileptic patients.
Ataee
P
P
Electrical and Computer Engineering, University of Tehran, Iran. ataee@ieee.org
Yazdani
A
A
Setarehdan
S
S
Noubari
H A
HA
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D012016:Reference Values; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity