Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9106
Main Title: Presenting a Spatial-Geometric EEG Feature to Classify BMD and Schizophrenic Patients
Author(s): Alimardani, Fatemeh
Boostani, Reza
Blankertz, Benjamin
Type: Article
Language Code: en
Abstract: Schizophrenia (SZ) and bipolar mood disorder (BMD) patients demonstrate some similar signs and symptoms; therefore, distinguishing those using qualitative criteria is not an easy task especially when these patients experience manic or hallucination phases. This study is aimed at classifying these patients by spatial analysis of their electroencephalogram (EEG) signals. In this way, 22-channels EEG signals were recorded from 52 patients (26 patients with SZ and 26 patients with BMD). No stimulus has been used during the signal recording in order to investigate whether background EEGs of these patients in the idle state contain discriminative information or not. The EEG signals of all channels were segmented into stationary intervals called “frame” and the covariance matrix of each frame is separately represented in manifold space. Exploiting Riemannian metrics in the manifold space, the classification of sample covariance matrices is carried out by a simple nearest neighbor classifier. To evaluate our method, leave one patient out cross validation approach has been used. The achieved results imply that the difference in the spatial information between the patients along with control subjects is meaningful. Nevertheless, to enhance the diagnosis rate, a new algorithm is introduced in the manifold space to select those frames which are less deviated around the mean as the most probable noise free frames. The classification accuracy is highly improved up to 98.95% compared to the conventional methods. The achieved result is promising and the computational complexity is also suitable for real time processing.
URI: https://depositonce.tu-berlin.de/handle/11303/10116
http://dx.doi.org/10.14279/depositonce-9106
Issue Date: 2016
Date Available: 14-Oct-2019
DDC Class: 610 Medizin und Gesundheit
Subject(s): bipolar mood disorder
EEG classification
noise detection
Riemannian geometric mean
schizophrenia
weighting
spatial topographic difference
License: https://creativecommons.org/licenses/by-sa/4.0/
Journal Title: International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems
Publisher: International Science and Engineering Society
Publisher Place: Brno
Volume: 5
Issue: 2
Publisher DOI: 10.11601/ijates.v5i2.143
Page Start: 79
Page End: 85
EISSN: 1805-5443
Appears in Collections:FG Neurotechnologie » Publications

Files in This Item:
File Description SizeFormat 
143-621-1-PB.pdf1.22 MBAdobe PDFThumbnail
View/Open


This item is licensed under a Creative Commons License Creative Commons