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

USDZQ Optimization Based on Ant Colony Algorithm and Application in ECG Compression

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。

This paper adopted an improved uniform quantizer, a uniform scalar dead zone quantizer（USDZQ）, to quantize the transformed coefficients. The selection of quantizer parameters directly affects the ECG data compression performanee. Therefore the objective of this study was focused on the optimization of the USDZQ parameters. We used the ant colony optimization（ACO） algorithm for the optimization. Experiments on several records from the MIT-BIH arrhythmia database show that as long as the USDZQ parameter is optimized reasonably,USDZQ can achieve better per formance than the uniform quantizer,and may successfully be applied in the ECG data compression.