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A new hybrid algorithm for ECG signal compression based on the wavelet transformation of the linearly predicted error.

机译：基于线性预测误差的小波变换的心电信号压缩混合算法。

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摘要

This paper describes a hybrid technique based on the combination of wavelet transform and linear prediction to achieve very effective electrocardiogram (ECG) data compression. First, the ECG signal is wavelet transformed using four different discrete wavelet transforms (Daubechies, Coiflet, Biorthogonal and Symmlet). All the wavelet transforms are based on dyadic scales and decompose the ECG signals into five detailed levels and one approximation. Then, the wavelet coefficients are linearly predicted, where the error corresponding to the difference between these coefficients and the predicted ones is minimized in order to get the best predictor. In particular, the residuals of the wavelet coefficients are uncorrelated and hence can be represented with fewer bits compared to the original signal. To further increase the compression rate, the residual sequence obtained after linear prediction is coded using a newly developed coding technique. As a result, a compression ratio (Cr) of 20 to 1 is achieved with percentage root-mean square difference (PRD) less than 4%. The algorithm is compared to an alternative compression algorithm based on the direct use of wavelet transforms. Experiments on selected records from the MIT-BIH arrhythmia database reveal that the proposed method is significantly more efficient in compression. The proposed compression scheme may find applications in digital Holter recording, in ECG signal archiving and in ECG data transmission through communication channels.
机译：本文介绍了一种基于小波变换和线性预测相结合的混合技术，可实现非常有效的心电图（ECG）数据压缩。首先，使用四个不同的离散小波变换（Daubechies，Coiflet，Biorthogonal和Symmlet）对ECG信号进行小波变换。所有的小波变换都基于二进阶，并将ECG信号分解为五个详细级别和一个近似值。然后，对小波系数进行线性预测，其中将与这些系数和预测系数之间的差相对应的误差最小化，以便获得最佳预测值。特别地，小波系数的残差是不相关的，因此与原始信号相比，可以用更少的比特来表示。为了进一步提高压缩率，使用新开发的编码技术对线性预测后获得的残差序列进行编码。结果，在均方根差百分比（PRD）小于4％的情况下实现了20∶1的压缩比（Cr）。该算法与基于直接使用小波变换的替代压缩算法进行了比较。对MIT-BIH心律失常数据库中选定记录的实验表明，该方法在压缩方面效率更高。所提出的压缩方案可以在数字动态心电记录，ECG信号归档以及通过通信信道的ECG数据传输中找到应用。

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