EEG signal compression based on classified signature and envelope vector sets
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CitationGürkan, 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
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.
Source2007 18th European Conference on Circuit Theory and Design
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