Rate distortion optimal ECG signal compression

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 (EGG) signals. ECG signal compression has traditionally been tackled by heuristic approaches. However, it has been demonstrated 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 number of bits. Compared to many other compression methods, we report superior performance for this method.