Lossless Compression Schemes for ECG Signals Using Neural Network Predictors
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Research Article Lossless Compression Schemes for ECG Signals Using Neural Network Predictors R. Kannan and C. Eswaran Center for Multimedia Computing, Faculty of Information Technology, Multimedia University, Cyberjaya 63100, Malaysia Received 24 May 2006; Revised 22 November 2006; Accepted 11 March 2007 Recommended by William Allan Sandham 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. Copyright © 2007 R. Kannan and C. Eswaran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
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 [2–6], subband coding [7, 8], transform coding [9–13], and vector quantization [14, 15], are applied for a wide range of one-, two-, and threedimensional signals. Lossless compression schemes are preferable to lossy compression schemes in biomedical applications where even the slight distortion of the signal may result in erroneous diagnosis. The application of lossless compression for ECG signals is motivated by the following factors. (i) A lossy compression scheme is likely to yield a poor reconstruction for a
specific portion of the ECG signal, which may be important for a specific diagnostic application. Furthermore, a lossy compression method may not yield diagnostically acceptable result
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