Ten lectures on wavelets
EEG analysis in a telemedical virtual world
Future Generation Computer Systems - Special issue on ITIS—an international telemedical information society
Compression and Coding Algorithms
Compression and Coding Algorithms
A High Performance Scheme for EEG Compression Using a Multichannel Model
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
Introduction to Data Compression, Third Edition (Morgan Kaufmann Series in Multimedia Information and Systems)
System architecture of a wireless body area sensor network for ubiquitous health monitoring
Journal of Mobile Multimedia
International Journal of Telemedicine and Applications
Embedded image coding using zerotrees of wavelet coefficients
IEEE Transactions on Signal Processing
Context-based lossless and near-lossless compression of EEG signals
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
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In recent years, there has been a growing interest in the compression of electroencephalographic (EEG) signals for telemedical and ambulatory EEG applications. Data compression is an important factor in these applications as a means of reducing the amount of data required for transmission. Allowing for a carefully controlled level of loss in the compression method can provide significant gains in data compression. Quantisation is easy to implement method of data reduction that requires little power expenditure. However, it is a relatively simple, non-invertible operation, and reducing the bit-level too far can result in the loss of too much information to reproduce the original signal to an appropriate fidelity. Other lossy compression methods allow for finer control over compression parameters, generally relying on discarding signal components the coder deems insignificant. SPIHT is a state of the art signal compression method based on the Discrete Wavelet Transform (DWT), originally designed for images but highly regarded as a general means of data compression. This paper compares the approaches of compression by changing the quantisation level of the DWT coefficients in SPIHT, with the standard thresholding method used in SPIHT, to evaluate the effects of each on EEG signals. The combination of increasing quantisation and the use of SPIHT as an entropy encoder has been shown to provide significantly improved results over using the standard SPIHT algorithm alone.