Adaptive data compression of ambulatory ECG using multi templates

Proposes a new adaptive method of data compression for digital ambulatory electrocardiograms (ECGs), considering the diagnostic significance of each segment of the ECG. The R-wave is detected, followed by multi-template matching of the detected beat and judgment of the noise level; the templates are successively created during processing. The residual signal (the difference between the original ECG and the best-fit template) is approximated with the FAN data compression method SAPA2 (Scan-Along Polygonal Approximation) and then encoded. The error threshold of FAN is decreased during the P-wave segments and increased during the noise segments; the maximum error of the reconstructed signal at each time is known. This method is applied to ECGs of the AHA (American Heart Association) database and its usefulness is indicated; e.g. the bit rate is approximately 400 bps at 8% PRD (percent RMS difference) and 200 bps at 15% PRD