Special Issue Paper
EEG signal compression based on classified signature and envelope vector sets
Version of Record online: 19 AUG 2008
Copyright © 2008 John Wiley & Sons, Ltd.
International Journal of Circuit Theory and Applications
Special Issue: ECCTD 2007
Volume 37, Issue 2, pages 351–363, March 2009
How to Cite
Gürkan, H., Guz, U. and Yarman, B. S. (2009), EEG signal compression based on classified signature and envelope vector sets. Int. J. Circ. Theor. Appl., 37: 351–363. doi: 10.1002/cta.548
- Issue online: 16 FEB 2009
- Version of Record online: 19 AUG 2008
- Scientific Research Fund of ISIK University. Grant Number: 06B302
In this paper, a novel method to compress electroencephalogram (EEG) signal is proposed. The proposed method is based on the generation process of the classified signature and envelope vector sets (CSEVS), which employs an effective k-means clustering algorithm. It is assumed that both the transmitter and the receiver units have the same CSEVS. In this work, on a frame basis, EEG signals are modeled by multiplying only three factors called as classified signature vector, classified envelope vector, and gain coefficient (GC), respectively. In other words, every frame of an EEG signal is represented by two indices R and K of CSEVS and the GC. EEG signals are reconstructed frame by frame using these numbers in the receiver unit by employing the CSEVS. The proposed method is evaluated by using some evaluation metrics that are commonly used in this area such as root-mean-square error, percentage root-mean-square difference, and measuring with visual inspection. The performance of the proposed method is also compared with the other methods. It is observed that the proposed method achieves high compression ratios with low-level reconstruction error while preserving diagnostic information in the reconstructed EEG signal. Copyright © 2008 John Wiley & Sons, Ltd.