Adaptive filter theory
The data compression book (2nd ed.)
The data compression book (2nd ed.)
A High Performance Scheme for EEG Compression Using a Multichannel Model
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
Context Based Error Modeling for Lossless Compression of EEG Signals Using Neural Networks
Journal of Medical Systems
Adaptive threshold-based block classification in medical image compression for teleradiology
Computers in Biology and Medicine
Efficient FPGA implementation of DWT and modified SPIHT for lossless image compression
Journal of Systems Architecture: the EUROMICRO Journal
Information Sciences: an International Journal
Compressive sampling of EEG signals with finite rate of innovation
EURASIP Journal on Advances in Signal Processing
Context-based lossless and near-lossless compression of EEG signals
IEEE Transactions on Information Technology in Biomedicine
Telemedicine system using computed tomography van of high-speed telecommunication vehicle
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals
IEEE Transactions on Information Technology in Biomedicine
Lossless compression of continuous-tone images via context selection, quantization, and modeling
IEEE Transactions on Image Processing
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Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.