Segmentation of digital signals based on estimated compression ratio

We have studied the problem of approximating a digital signal with a suitable continuous broken line, We use the approximative broken line for further analysis of the signal as detection of peaks, waves, and other structural features, We can also save considerable amount of storage space with an approximation that does not lose too much significant information about the original signal, Our work is based on examining different distance metrics and different segmentation methods with respect to the remaining residual error in the resulting approximation, The aim of the work has been to develop a method that can perform segmentation with an acceptable amount of residual error without a need to define a large set of parameters that control the segmentation process, Our contribution is to examine the effect of the estimated compression ratio of the resulting approximation and finding an estimate of this compression ratio, We first define a target in the form of a compression ratio of the resulting approximation and then by applying our method, try to find a suitable threshold parameter to achieve this target, We have tested our method with electrocardiogram (EGG) signals and the compression ratio of the approximation has been found to be a Suitable target to control the segmentation process.