Estimation of neurological status from non-electroencephalography bio-signals by motif patterns
[ X ]
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, a novel feature extraction approach, which was called motif patterns, was proposed and it was employed to estimate the neurological status from non-electroencephalography (non-EEG) bio-signals. It was found from the literature that successful results were obtained by using the feature extraction methods that are sensitive to local changes such as one-dimensional local binary patterns (1D-LBP). In 1D-LBP, the local changes in a signal were determined based on the comparisons between each central value'' with its neighbors. In order to increase the sensitivity of extracted features from the local changes in a signal, each value'' in the signal was compared with its neighbor, and by this way, a motif was obtained in the result of the comparisons in a specified window. To evaluate and validate the proposed approach, the non-EEG bio-signals, which were recorded by electrodermal activity, temperature, accelerometer, heart rate, and arterial oxygen level sensors, were employed. The features that were extracted from these signals by the proposed motif patterns were classified by machine learning methods. The neurological status of each of the samples was classified accurately by the proposed approach. Furthermore, the optimal sensor types were investigated and it was found that heart rate signals are enough to estimate the neurological status. (C) 2019 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Motif patterns, One-dimensional local binary patterns, Local changes, Neurological status, Bio-signals, Feature extraction
Kaynak
Applied Soft Computing
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
83
