Paper Info
Title
Classification Enhancement For Post-Stroke Dementia Using Fuzzy Neighborhood Preserving Analysis With Qr-Decomposition
Abstract
The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037531
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Dimensionality reduction,Pattern recognition,Feature selection,Computer science,Support vector machine,Speech recognition,Tsallis entropy,Independent component analysis,Artificial intelligence,Electroencephalography,Wavelet,Wavelet transform
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
5
4
Name
Order
Citations
PageRank
Noor Kamal Al-Qazzaz121.05
Sawal H. M. Ali2142.20
A. Siti Anom3458.59
Escudero Javier417427.45