A time-frequency adaptive signal model-based approach for parametric ECG compression

A preliminary investigation of an atomic model-based algorithm for the compression of single lead ECG is presented. The paper presents a novel coding scheme for ECG signals based on time-frequency atomic signal representations. Signal-adaptive parametric models based on over-complete dictionaries of time-frequency atoms are considered. Such over-complete expansions are here derived using the matching pursuit algorithm. The compression algorithm has been evaluated with the MIT-BIH Arrhythmia Database. Our algorithm was compared with several well-known ECG compression algorithms and was found to be superior at all tested bit rates. An average compression rate of approximately 140 bps (compression ratio of about 28:1) has been achieved with a good reconstructed signal quality (PRD of about 7%).