EEG signal compression based on Classified Signature and Envelope Vector Sets

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.