A novel approach for SEMG signal classification with adaptive local binary pattern
Yükleniyor...
Tarih
2015-12-31
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Nature
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.
Açıklama
Anahtar Kelimeler
Adaptive Signal Processing, Biomedical Signal Processing, Extracting Local Features, Feature Extraction, Local Binary Patterns, Time Signals
Kaynak
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
54
Sayı
7
Künye
Ertuğrul, Ö F., Kaya, Y., Tekin, R. (2015). A novel approach for SEMG signal classification with adaptive local binary patterns. Medical & Biological Engineering & Computing, 54(7), pp. 1137-1146. https://doi.org/10.1007/s11517-015-1443-z
