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Lossless Compression Schemes for ECG Signals Using Neural Network Predictors
2007
EURASIP Journal on Advances in Signal Processing
This paper presents lossless compression schemes for ECG signals based on neural network predictors and entropy encoders. Decorrelation is achieved by nonlinear prediction in the first stage and encoding of the residues is done by using lossless entropy encoders in the second stage. Different types of lossless encoders, such as Huffman, arithmetic, and runlength encoders, are used. The performances of the proposed neural network predictor-based compression schemes are evaluated using standard
doi:10.1155/2007/35641
fatcat:hvytp2kdizcwdcz56q6cwxpxye