Classification of transcranial Doppler signals using their chaotic invariant measures
Küçük Resim Yok
Tarih
2007
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER IRELAND LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, chaos analysis was performed on the transcranial Doppler (TCD) signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. Two chaotic invariant measures, i.e. the maximum Lyapunov exponent and the correlation dimension, were calculated for the TCD signals after applying nonlinearity and stationarity tests to them. The sonograms obtained via Burg autoregressive (AR) method demonstrated that the chaotic invariant measures represented the unpredictability and complexity levels of the TCD signals. According to the multiple linear regression analysis, the chaotic invariant measures were found to be highly significant for the regression equation which fitted to the data. This result suggested that the chaotic invariant measures could be used for automatically differentiating various cerebrovascular conditions via an appropriate classifier. For comparison purposes, we investigated several different classification algorithms. The k-nearest neighbour algorithm outperformed all the other classifiers with a classification accuracy of 94.44% on the test data. We used the receiver operating characteristic (ROC) curves in order to assess the performance of the classifiers. The results suggested that the classification systems which use the chaotic invariant measures as input have potential in detecting the blood flow velocity changes due to various brain diseases. (c) 2007 Elsevier Ireland Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
transcranial Doppler signals, nonlinear analysis, chaotic measures, classification, NEFCLASS, decision trees, K-nearest neighbour, multilayer perceptron
Kaynak
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
86
Sayı
2
