Segmentation of digital signals based on estimated compression ratio

The authors have studied the problem of approximating a digital signal with a suitable continuous broken line. They use the approximative broken line for further analysis of the signal as detection of peaks, waves, and other structural features. They can also save considerable amount of storage space with an approximation that does not lose too much significant information about the original signal. The authors' 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. The authors' contribution is to examine the effect of the estimated compression ratio of the resulting approximation and finding an estimate of this compression ratio. They first define a target in the form of a compression ratio of the resulting approximation and then by applying their method, try to find a suitable threshold parameter to achieve this target. The authors have tested their 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.