Nygaard R, Melnikov G, Katsaggelos A K
Stavanger University College, Department of Electrical and Computer Engineering, 2557 Ullandhaug, 4091 Stavanger, Norway.
IEEE Trans Biomed Eng. 2001 Jan;48(1):28-40. doi: 10.1109/10.900246.
Signal compression is an important problem encountered in many applications. Various techniques have been proposed over the years for addressing the problem. In this paper, we present a time domain algorithm based on the coding of line segments which are used to approximate the signal. These segments are fit in a way that is optimal in the rate distortion sense. Although the approach is applicable to any type of signal, we focus, in this paper, on the compression of electrocardiogram (ECG) signals. ECG signal compression has traditionally been tackled by heuristic approaches. However, it has been demonstrated [1] that exact optimization algorithms outperform these heuristic approaches by a wide margin with respect to reconstruction error. By formulating the compression problem as a graph theory problem, known optimization theory can be applied in order to yield optimal compression. In this paper, we present an algorithm that will guarantee the smallest possible distortion among all methods applying linear interpolation given an upper bound on the available number of bits. Using a varied signal test set, extensive coding experiments are presented. We compare the results from our coding method to traditional time domain ECG compression methods, as well as, to more recently developed frequency domain methods. Evaluation is based both on percentage root-mean-square difference (PRD) performance measure and visual inspection of the reconstructed signals. The results demonstrate that the exact optimization methods have superior performance compared to both traditional ECG compression methods and the frequency domain methods.
信号压缩是许多应用中遇到的一个重要问题。多年来已经提出了各种技术来解决这个问题。在本文中，我们提出了一种基于线段编码的时域算法，这些线段用于逼近信号。这些线段以在率失真意义上最优的方式拟合。尽管该方法适用于任何类型的信号，但在本文中，我们专注于心电图（ECG）信号的压缩。传统上，ECG信号压缩是通过启发式方法解决的。然而，[1]已经证明，精确优化算法在重建误差方面比这些启发式方法有很大优势。通过将压缩问题表述为图论问题，可以应用已知的优化理论来实现最优压缩。在本文中，我们提出了一种算法，在给定可用比特数上限的情况下，该算法将保证在所有应用线性插值的方法中产生最小可能的失真。使用一个多样化的信号测试集，我们进行了广泛的编码实验。我们将编码方法的结果与传统的时域ECG压缩方法以及最近开发的频域方法进行比较。评估基于百分比均方根差（PRD）性能指标以及对重建信号的目视检查。结果表明，精确优化方法与传统ECG压缩方法和频域方法相比具有优越的性能。
