Discrete-time signal processing
Discrete-time signal processing
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
Lossless compression schemes for ECG signals using neural network predictors
EURASIP Journal on Applied Signal Processing
Compressive sampling of EEG signals with finite rate of innovation
EURASIP Journal on Advances in Signal Processing
Arithmetic coding in lossless waveform compression
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
An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals
IEEE Transactions on Information Technology in Biomedicine
An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression
Computers in Biology and Medicine
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A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.