A channel differential EZW coding scheme for EEG data compression

Research and improvement of ECG compression algorithm based on EZW

Embedded zerotree wavelet (EZW) is an efficient compression method that has advantages in coding, but its multilayer structure information coding reduces signal compression ratio. This paper studies the optimization of the EZW compression algorithm and aims to improve it. First, we used lifting wavelet transformation to process electrocardiograph (ECG) signals, focusing on the lifting algorithm. Second, we utilized the EZW compression coding algorithm, through the ECG information decomposition to determine the feature detection value. Then, according to the feature information, we weighted the wavelet coefficients of ECG (through the coefficient as a measure of weight) to achieve the goal of improved compression benefit.

EEG compression of scalp recordings based on dipole fitting

A novel technique for electroencephalogram (EEG) compression is proposed in this paper. This technique models the intrinsic dependence inherent between the different EEG channels. It is based on methods borrowed from dipole fitting that is usually used in order to find a solution to the classic problems in EEG analysis: inverse and forward problems. To compress the EEG signals, the forward model based on approximated source dipoles is first used to provide an approximation of the recorded signals. Then, (based on a smoothness factor) appropriate coding techniques are suggested to compress the residuals of the fitting process. Results show that this technique works well for different recordings and for different patients, and is even able to provide near-lossless compression for certain types of recordings.