Complete coding scheme using optimal time domain ECG compression methods

Traditionally, compression of digital ElectroCardio-Gram (ECG) signals has been tackled by heuristical approaches. However, it has recently been demonstrated that exact optimization algorithms perform much better with respect to reconstruction error. Time domain compression algorithms are based on the idea of extracting representative signal samples from the original signal. As opposed to the heuristical approaches, the exact time domain compression algorithms are based upon a sound mathematical foundation. By formulating the sample selection problem as a graph theory problem, optimization theory can be applied in order to yield optimal compression. The signal is reconstructed by interpolation among the extracted signal samples. Different interpolation methods have been implemented, such as linear interpolation [1] and second order polynomial reconstruction [2]. In order to compare the performance of the two algorithms in a fully justified way, the results have to be encoded. In this paper we develop an efficient encoding method based on entropy coding for that purpose. The results prove good performance of the exact optimization methods in comparison with traditional time domain compression methods.