EEG signal compression based on classified signature and envelope vector sets
Yükleniyor...
Dosyalar
Tarih
2007
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE Computer Society
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, a novel method to compress ElectroEncephaloGram (EEG) Signal is proposed. The proposed method is based on the generation Classified Signature and Envelope Vector Sets (CSEVS) by using an effective k-means clustering algorithm. In this work on a frame basis, any EEG signal is modeled by multiplying three parameters as called the Classified Signature Vector, Classified Envelope Vector, and Frame-Scaling Coefficient. In this case, EEG signal for each frame is described in terms of the two indices R and K of CSEVS and the frame-scaling coefficient. The proposed method is assessed through the use of root-mean-square error (RMSE) and visual inspection measures. The proposed method achieves good compression ratios with low level reconstruction error while preserving diagnostic information in the reconstructed EEG signal.
Açıklama
Anahtar Kelimeler
Biomedical signal processing, Brain modeling, Circuit theory, Classified envelope vector, Classified signature and envelope vector sets, Classified signature vector, Clustering algorithms, Computational complexity, Computer science, Data compression, Diagnostic information, Educational institutions, EEG signal compression, EEG signals, Electric variables measurement, Electrocardiography, Electroencephalogram signal, Electroencephalogram signals, Electroencephalography, Envelope vector sets, Error reconstruction, Frame-scaling coefficient, Inspection, K-means clustering, K-means clustering algorithm, Mean square error, Mean square error methods, Medical signal processing, Monitoring, Reconstruction error, Root mean square errors, Root-mean-square error, Scaling coefficients, Signal compression, Signal reconstruction, Signature vectors, Speech, Three parameters, Vectors, Visual inspection, Visual inspection measures
Kaynak
2007 18th European Conference on Circuit Theory and Design
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Gürkan, H., Güz, Ü. & Yarman, B. S. B. (2007). EEG signal compression based on classified signature and envelope vector sets. Paper presented at the 2007 18th European Conference on Circuit Theory and Design, 420-423. doi:10.1109/ECCTD.2007.4529622
