Abstract
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 distortion and compression efficiency measures. Selected records from MIT-BIH arrhythmia database are used for performance evaluation. The proposed compression schemes are compared with linear predictor-based compression schemes and it is shown that about 11% improvement in compression efficiency can be achieved for neural network predictor-based schemes with the same quality and similar setup. They are also compared with other known ECG compression methods and the experimental results show that superior performances in terms of the distortion parameters of the reconstructed signals can be achieved with the proposed schemes.
Highlights
Any signal compression algorithm should strive to achieve greater compression ratio and better signal quality without affecting the diagnostic features of the reconstructed signal
The lossless compression schemes proposed in this paper can be applied to a wide variety of biomedical signals including ECG and they yield good signal quality at reduced compression efficiency compared to the known lossy compression methods
We have evaluated the quality and compression efficiency performances of the following five schemes using adaptive-block training (ABT) and single-block training (SBT) training methods: (i) single-stage scheme with multilayer perceptron (MLP) as the predictor; (ii) two-stage scheme with MLP predictor in the first stage and Huffman encoder in the second stage; (iii) two-stage scheme with MLP predictor in the first stage followed by runlength and Huffman encoders in the second stage; (iv) two-stage scheme with MLP predictor in the first stage and arithmetic encoder in the second stage; (v) two-stage scheme with MLP predictor in the first stage followed by runlength and arithmetic encoders in the second stage
Summary
Any signal compression algorithm should strive to achieve greater compression ratio and better signal quality without affecting the diagnostic features of the reconstructed signal. Several methods have been proposed for lossy compression of ECG signals to achieve these two essential and conflicting requirements. Some techniques such as the amplitude zone time epoch coding (AZTEC), the coordinate reduction time encoding system (CORTES), the turning point (TP), and the fan algorithm are dedicated and applied only for the compression of ECG signals [1] while other techniques, such as differential pulse code modulation [2,3,4,5,6], subband coding [7, 8], transform coding [9,10,11,12,13], and vector quantization [14, 15], are applied for a wide range of one-, two-, and threedimensional signals. The lossless compression schemes proposed in this paper can be applied to a wide variety of biomedical signals including ECG and they yield good signal quality at reduced compression efficiency compared to the known lossy compression methods
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