基于蚁群算法的带截止区均匀量化器的优化及其在ECG数据压缩中的应用

结合小波系数的特点,采用了改进的均匀量化器——带截止区的均匀量化器(USDZQ)对变换后的小波系数进行量化。量化器的参数选取直接影响到ECG数据压缩的质量和压缩比,因此重点研究了USDZQ的参数优化问题,选取了蚁群优化算法(ACO)作为USDZQ参数的优化工具。最后,利用本文算法对MIT-BIH心律失常数据库的ECG信号进行了编码测试。实验结果表明,只要对USDZQ的参数进行合理优化,USDZQ就能获得优于均匀量化的性能,并可以成功地应用于ECG数据压缩中。

Contribution to S-EMG Signal Compression in 1D by the Combination of the Modified Discrete Wavelet Packet Transform (MDWPT) and the Discrete Cosine Transform (DCT)

A new Modified Discrete Wavelets Packets Transform (MDWPT) based method for the compression of Surface EMG signal (s-EMG) data is presented. A Modified Discrete Wavelets Packets Transform (MDWPT) is applied to the digitized s-EMG signal. A Discrete Cosine Transforms (DCT) is applied to the MDWPT coefficients (only on detail coefficients). The MDWPT+ DCT coefficients are quantized with a Uniform Scalar Dead-Zone Quantizer (USDZQ). An arithmetic coder is employed for the entropy coding of symbol streams. The proposed approach was tested on more than 35 actuals S-EMG signals divided into three categories. The proposed approach was evaluated by the following parameters: Compression Factor (CF), Signal to Noise Ratio (SNR), Percent Root mean square Difference (PRD), Mean Frequency Distortion (MFD) and the Mean Square Error (MSE). Simulation results show that the proposed coding algorithm outperforms some recently developed s-EMG compression algorithms.