A novel family of compression algorithms for ECG and other semiperiodical, one-dimensional, biomedical signals.

In this paper, a novel family of compression algorithms is presented, which is designed to exploit the redundancy of one-dimensional (1-D) semiperiodical biomedical signals resulting from the cyclic nature of the underlying physical process. The basic idea is that a pool of past-seen cycles is maintained and cycles to be encoded can be stored as transformed versions of those residing in the pool. Conceptually, this approach is an extension of dictionary-based coding schemes used for text compression to signal patterns residing in an n-dimensional space. A cycle transformation method is introduced in order to render the pattern matching process practical and to enable cycle substitution. Based on the principles of the algorithmic family and this transformation method, an electrocardiogram (ECG)-oriented algorithm is implemented and thoroughly tested. The performance of this implementation is examined theoretically and deductions about the optimal algorithm settings are made. The ECG compression algorithm is superior to the average beat subtraction algorithm as proposed by Hamilton and Tompkins in cases where high compression ratios are required.