A novel Electroencephalogram (EEG) data compression technique
Year: 2008
Type: conference-paper
Abstract: 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.
Authors: Hakan Gurkan, Umit Guz, B. Siddik Yarman
Institution: Istanbul University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2167367015