Al-Ajlouni A F, Abo-Zahhad M, Ahmed S M, Schilling R J
Communication Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan.
J Med Eng Technol. 2008 Nov-Dec;32(6):425-33. doi: 10.1080/03091900701455763.
Compression of electrocardiography (ECG) is necessary for efficient storage and transmission of the digitized ECG signals. Discrete wavelet transform (DWT) has recently emerged as a powerful technique for ECG signal compression due to its multi-resolution signal decomposition and locality properties. This paper presents an ECG compressor based on the selection of optimum threshold levels of DWT coefficients in different subbands that achieve maximum data volume reduction while preserving the significant signal morphology features upon reconstruction. First, the ECG is wavelet transformed into m subbands and the wavelet coefficients of each subband are thresholded using an optimal threshold level. Thresholding removes excessively small features and replaces them with zeroes. The threshold levels are defined for each signal so that the bit rate is minimized for a target distortion or, alternatively, the distortion is minimized for a target compression ratio. After thresholding, the resulting significant wavelet coefficients are coded using multi embedded zero tree (MEZW) coding technique. In order to assess the performance of the proposed compressor, records from the MIT-BIH Arrhythmia Database were compressed at different distortion levels, measured by the percentage rms difference (PRD), and compression ratios (CR). The method achieves good CR values with excellent reconstruction quality that compares favourably with various classical and state-of-the-art ECG compressors. Finally, it should be noted that the proposed method is flexible in controlling the quality of the reconstructed signals and the volume of the compressed signals by establishing a target PRD and a target CR a priori, respectively.
为了高效存储和传输数字化心电图（ECG）信号，对其进行压缩是必要的。离散小波变换（DWT）由于其多分辨率信号分解和局部性特性，最近已成为一种强大的ECG信号压缩技术。本文提出了一种基于选择不同子带中DWT系数最佳阈值水平的ECG压缩器，该压缩器在重建时能在保留重要信号形态特征的同时实现最大的数据量减少。首先，将ECG进行小波变换到m个子带，并且使用最优阈值水平对每个子带的小波系数进行阈值处理。阈值处理去除了过小的特征并用零来替代它们。为每个信号定义阈值水平，以便对于目标失真使比特率最小化，或者对于目标压缩率使失真最小化。阈值处理之后，使用多嵌入式零树（MEZW）编码技术对得到的重要小波系数进行编码。为了评估所提出压缩器的性能，以均方根误差百分比（PRD）和压缩率（CR）来衡量，对来自MIT - BIH心律失常数据库的记录在不同失真水平下进行压缩。该方法实现了良好的CR值以及优异的重建质量，与各种经典的和最新的ECG压缩器相比具有优势。最后，应该注意的是，所提出的方法通过分别事先设定目标PRD和目标CR，在控制重建信号质量和压缩信号量方面具有灵活性。
